A key element in DiDi’s strategy is building a smart transportation ecosystem to capture future opportunities, with global expansion through alliances with regional partners. Source: DiDi. Click to enlarge.
From its founding as a taxi-hailing business, DiDi has been building up a world-leading one-stop transportation platform since 2012. The company continues to apply its big data capabilities to increase taxi drivers’ work efficiency and income. With 2 million taxi drivers connected to the app, DiDi is now the world’s leading online platform for taxi-hailing. In 2017, taxi drivers completed 1.1 billion rides on DiDi. DiDi is also working closely with taxi companies to help them build intelligent IT and driver management systems. Currently DiDi has established partnerships with about 500 taxi operators in China.
Separately, in Beijing, DiDi launched its car-sharing platform. DiDi is partnering with automakers, new energy transportation infrastructure operators and after-sales service providers to build an open new energy car-sharing system. The network of strategic partners includes 12 top automakers including BAIC BJEV, BYD, Chang’an Automobile Group, Chery Automobile Group, Dongfeng Passenger Vehicle, First Auto Works, Geely Auto, Hawtai Motor, JAC Motors, KIA Motors, Renault-Nissan-Mitsubishi, and Zotye Auto.
According to a study by GM Insights, the global car-sharing market is expected to grow 34% annually from 2017 to 2024, while the annual growth rate in China will exceed 40%. The first generation of large-scale, new energy car-sharing platforms are expected to materialize in core emerging countries such as China.
DiDi hopes to leverage on its AI strengths and national network to empower the entire automotive industry chain. The company’s data analytics capabilities enable smarter network management based on dynamic understanding of user distribution and attributes. Under the partnership, DiDi will open its platform to automakers’ own sharing services. The platform will introduce to individuals and corporate partners not only diversified models from automakers, but also auto-related finance and insurance services.
In addition to automakers, DiDi will also work closely with other car-sharing services, rental companies, infrastructure operators and after-sales service providers. As of August 2017, DiDi—which acquired Uber China in 2016—had built investment and technology partnerships with seven leading rideshare companies of the world, including Lyft, Grab, Ola, Uber, 99, Taxify and Careem.
DiDi believes the new program will reduce cost and enhance efficiency for the entire industry chain by integrating resources from cars, capital, parking spaces, charging points and refueling stations, to auto-maintenance and repair services in a new, open ecosystem of collaboration.
Uber uses a master algorithm to determine how much money its drivers make—and women are ending up with less.
The gap: In a study released today of over 1.8 million drivers on the platform, women were found to earn $1.24 per hour less than men. Women also earned $130 less per week on average, in part because they tend to drive fewer hours.
The cause: The study, which was carried out by researchers at Stanford and Uber and has not undergone peer review, attributed the difference in pay to fact that male Uber drivers:
—Are more likely to drive in higher-paying locations
—Take on trips with shorter distances to the rider
—Chose to drive longer trips
All of these are variables in the formula Uber uses to calculate driver wages, and the study showed they all tilted in men’s favor (the study claims men earn $21.28 an hour, on average). Women also have higher turnover on the platform, and more experienced drivers tend to get higher pay.
Though it wasn’t covered in the study, one reason women may avoid higher-paying areas is that they don’t feel safe—they may opt not to drive late at night in certain places, for instance, or stay away from neighborhoods that are considered dangerous.
Closing the gap: The study shows there’s a persistent disparity in pay by gender, and Uber may have a hard time fixing it. Stanford economist Rebecca Diamond, one of the paper’s coauthors, says the researchers considered recommending taking speed out of the equation, for example. But as she says, “both riders and drivers would prefer to arrive at the destination sooner.”
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Didi is becoming big in big data and, according to latest announcements, their new project is set to change how cities look at mobility. The Chinese ride-hailing giant has just launched the “Didi Smart Transportation Brain,” a solution that brings data from government and other partners to develop a city traffic management powered by AI and cloud technology.
What the Brain is solving is not just traffic jams, it’s a huge city-sized puzzle. The project has been in development for around a year now, piloting in over 20 Chinese cities. It’s a multidisciplinary endeavor: it includes analyzing data from video cameras, sensors and GPS signals from Didi’s cars, installing intelligent traffic lights, working with local traffic police and city planners.
“The transportation industry is still nascent in terms of data analytics and what we are trying to do is be the frontrunner with DiDi’s network, dataset, infrastructure and technologies to push the frontier for transportation,” Liu Xidi, the head of Public Transportation Division at Didi Chuxing told TechNode during Didi’s Intelligent Transportation Summit in Beijing held on Thursday.
DiDi’s Intelligent Transportation Summit was held on January 25, 2018 in Beijing. (Image credit: Didi Chuxing)
Didi Chuxing claims it is the largest connected network in the world. The number of drivers that worked on their platform in 2017 was over 21 million, according to DiDi’s Senior Vice President Zhang Wensong who talked with TechNode. This huge number is transforming Didi into a different animal than its global competitor Uber and it’s not just implementing AI and big data to optimize their ride-hailing or solve traffic jams. Didi is now a total mobility company covering every aspect of mobility, from infrastructure to vehicles to humans.
Solving city traffic like Google’s AlphaGo
“Usually when we are compared to Uber we mostly pay attention to our technology and our product and we think Didi is a big data and a technology company. Our platform and our technology are probably most advanced in the world,” says Zhang. According to him, the complexity of the dispatching system makes the algorithms behind it extremely sophisticated, much more complicated than what Google’s AI software AlphaGo faces during Go games.
Zhang is a data man. A former CTO and Vice President of Alibaba Cloud Computing he knows his way around numbers and explains the problem that Didi faces in a numerical way:
Passenger A orders a ride and the system dispatches a driver. A millisecond later passenger B pops up and he is located much closer to the driver than passenger A. If the driver were to pick up passenger A instead of passenger B that wouldn’t be an optimized solution: time has been wasted. That’s why the system puts the two passengers in a queue and matches them with drivers that are closer to them.
The problem is that this solution remains the optimal one for a very short time: 2 seconds. After that, another passenger may place an order, in a couple of more seconds the fourth one, and so on. The system has to adapt within 2 seconds.
Traffic dynamics of 400 cities in 24 hours painted with DiDi’s big data. (Image credit: Didi Chuxing)
“This is just an optimized solution for 2 seconds but it’s not an optimized solution for 4 seconds or one minute so we need to anticipate the future,” Zhang explained. “Since we know each day has 86,400 seconds, if we divide it in 2 seconds there are 43,200 steps and we know Go is only 19 multiplied by 19 or 361 steps that’s why our problem is 100 times more complicated than Go.”
The AlphaGo comparison also translates to managing city traffic, according to Didi. The AI program was successful because it analyzed each and every game of Go in the history, including the most complicated ones. Didi is analyzing some of the world’s most complicated cities—China’s cities. Unlike urban centers in developed countries like the US that tend to be well-planned out, cities like Beijing or Manila are often chaotic.
More importantly for Didi’s ride-hailing service, passenger and driver needs are different in China than the US for instance where car ownership is more prevalent. This means DiDi can develop services that cater better to environments more similar to China which is the majority of the world. Cracking some of the messiest cases in China both in ride-hailing services and in smart traffic management means that they will have something valuable to offer during their global expansion.
A new product for globalization?
Previously unknown outside China, Didi has been making headway in their globalization goal. After abandoning its US project by turning over their business to Lyft, the company has invested in Brazilian ride-hailing startup 99. It has partnered with several other ride-hailing companies, including Grab in Southeast Asia, Ola in India and Taxify, which has a presence in Europe, Africa, and other regions.
“For smart transportation, we have actually talked to various government entities to tell them what we are trying to do, what we’re doing now and how far we’ve gone,” says Liu. “Most of them are very excited because congestion is not an Asian problem, it’s a global problem, especially in all the major cities—developed and developing.”
Liu Xidi, Head of Public Transportation Division, Smart Transportation Department (l) and Zhang Wensong, Senior Vice President at Didi Chuxing (r) showcasing the complexities of the Didi Brain. (Image credit: TechNode)
However, Liu stresses that the smart transportation division is still in development even though it now has around 200 employees on board. “We are still young, one year old, we are still growing and it takes time.”
The division is now focusing their efforts on Chinese cities, working with local traffic authorities to implement their project and with ministry-level researchers to create standards and policies. They are developing a couple of product lines or units including smart traffic lights, monitoring systems, and optimizing public transportation. Didi has also announced on Friday the opening of its third research institute, the new AI Labs in Beijing which will be led by Prof. Ye Jieping, Vice President of Didi Chuxing.
Although no such plans have been announced, it is easy to imagine that Didi will eventually want to monetize its project abroad and this would be a smart investment. Despite all that impressive data and shiny AI algorithms, many governments are reluctant to welcome companies such as Didi, Uber, and Lyft in fears of destroying the local taxi industry and creating a monopoly. Didi’s big data, and sharing of that data, might be a way for Didi to open these markets and assuage those fears.
After selling his previous company, Cleversafe, to IBM for more than $1.3 billion two years ago, it didn’t take Chris Gladwin long to re-enter the entrepreneurial fray.
Like Cleversafe, Gladwin’s new startup, Ocient, aims to introduce a new paradigm in how companies manage their data. And to him, there’s no better place to do that than in Chicago.
“The talent engine we built at Cleversafe was the reason we succeeded,” he said. “And a number of things about being in Chicago contributed to that.”
The talent engine we built at Cleversafe was the reason we succeeded.”
One important factor in that equation, said Gladwin, is employee loyalty. Last fall, Business Insider published an article about employee retention at the country’s 10 largest tech companies. Average tenures ranged from 1.2 years at Uber to 2 years at Facebook.
“At Cleversafe, employee longevity was more like 10 years,” said Gladwin. “I have a picture of the team from 10 years before IBM bought the company, and half the people were still there when the acquisition happened. Most of them are still there now.”
Moreover, he said, the quality of available talent is remarkable, thanks to nearby colleges like the University of Illinois Urbana-Champaign, University of Chicago, Illinois Institute of Technology and Northwestern University.
Seen together, these factors helped Cleversafe gain credibility with the large, technologically sophisticated clients it courted. In initial meetings with prospective clients, Gladwin said, it wasn’t uncommon for his company to send an engineer with years of tenure at the company and upward of 100 patents in their area of expertise.
“Then comes our competitor with someone who’s been there for 18 months, who inherited the code from someone else and who is still trying to figure it out,” said Gladwin.
We used to rely on sticking humans into the [data analytics process], but we’re transcending that.”
Big data, big opportunities — but also, big challenges
While exact details of what Ocient does are still under wraps, Gladwin’s new startup aims to help companies take on, and take advantage of, ever-growing volumes of data. As more and more devices amass increasingly granular information, Gladwin said, data sets containing more than a trillion data elements — that is, unique attributes about which data is gathered — will become commonplace.
“There are only a few organizations that analyze data at that scale today, but five years from now it will be the majority,” he said. “If you look at the databases, the data analytics tools and the operating systems — basically all the pieces you need to do that analysis — they’re just not designed for that.”
Nor, for that matter, is the hardware those tools currently run on. And while today’s cutting-edge technologies combine machine learning with selective human intervention, Gladwin said companies are now reaching the point where that strategy won’t work anymore.
“At that scale, data sets become incomprehensibly large,” said Gladwin. “You just can’t sit a human in front of that and expect them to do anything useful. We used to rely on sticking humans into the [data analytics] process to figure out what’s going on, but we’re transcending that.”
Some industries that generate particularly large amounts of data, like finance, adtech and genomic research, are already running into this problem.
Building a new big data paradigm won’t happen overnight. Like with Cleversafe, Gladwin said he won’t be surprised if it takes a decade to reach the end of Ocient’s current product roadmap.
“But we won’t wait that long before we start putting part of it in production,” he said. “The product will mature, and it will be put to use. But that won’t necessarily mean you’ll see any big announcements.”
With a current headcount in the high twenties, Ocient is rapidly growing its team. While Gladwin declined to speculate about how many hires he’d make in the year to come, he said multiple people started on the day of his interview with Built In.
The most important attribute Ocient looks for in prospective hires, said Gladwin, is a genuine passion for what they do.
“The one question we always ask developers is: ‘Of all the software you’ve written, other than things you’ve written for school or work, what’s your favorite and why?’” said Gladwin. “If you’re the type of person who loves to write software, and who does projects because you’re interested or passionate about it, it’s hard to shut you up. If you don’t have that passion, you won’t have an answer.”
What do consumer credit reporting agency Equifax and ride-hailing company Uber have in common?
One would imagine that as large enterprises, they would check the boxes for good cyber-security practices: a healthy security budget, deployment of leading-edge cyber security technologies, and round-the-clock monitoring by well-trained cyber professionals.
Yet they were revealed last year to have been successfully hacked.
Equifax was the victim of one of the biggest data breaches in history with about 145 million consumers’ data compromised, including credit card numbers. Uber revealed it was breached in 2016, losing about 57 million users’ and drivers’ information worldwide. To make things worse, it paid the hacker US$100,000 to delete the stolen data, and to keep the hack quiet.
Last year was a watershed year with an unprecedented number of cyber hacks, leaks and data breaches. We believe 2018 will be worse, as attackers become increasingly creative with attack methods and increasingly destructive payloads that better target system vulnerabilities. Why is this so?
ASYMMETRIC THREAT LANDSCAPE
First, the threat landscape will continue to be asymmetrical. Threat actors have an edge over enterprises that are hard-pressed to staff up internal cyber security teams.
State-sponsored actors and, increasingly, organised crime groups are well funded, organised and resourced. They can afford to take their time to do research on their target, create the right malware and tailor their attacks to their targets. Even if they were to fail the first time, they can persist to try again and again at very little marginal cost.
These entities are aided by the breathtaking rate of technological advancement, but attackers have also begun to acquire an increasingly deep understanding of human nature. This has manifest itself in more nuanced attacks that make use of social engineering and behavioural insights.
What we have seen in recent years is the continued evolution of (and preference for) very complex and precise spear phishing campaigns, unlike spam or phishing e-mails which are mass attacks. A spear phishing campaign targets specific individuals, organisations or businesses, to collect sensitive information. It may take the form of a professional-sounding, personalised e-mail that makes use of personal data collected from public posts on social media sites and blogs to target subjects to lower their guard – to entice them to click on suspicious links or open documents that may be virus-contaminated.
Another form of personalised attack is the watering-hole attack, which takes place when hackers ambush their targets at the websites they frequently visit. The hackers would inject a zero-day exploit – a malicious code that takes advantage of vulnerabilities that software developers and cyber security professionals are unaware of, giving them no time, or “zero days”, to prepare – on that website and lie in wait for their target.
When the target appears on the site, the exploit redirects the target to a different site where the malware is present and infects the organisation’s network. Once that is accomplished, the cyber criminal has access to the organisation’s network and is able to exfiltrate critical data, such as passwords and permissions, or pivot to attack other devices in the network.
The plain fact is that the adversaries sometimes understand us better than we do. They are in some ways more motivated to do harm than organisations are to protect their systems, in part because the rewards for breaching organisations can be greater than the gains from strengthening security.
Second, an extensive shadow industry is being created around hacking and data that will make it both easier and more lucrative to engage in such dark trades.
Hacking has created a shadow economy where data is bought and sold on the dark Web to organised cyber criminal syndicates. Data is the new oil. It is what threat actors are after, and what needs the most protection.
This has birthed a booming shadow economy. On top of personal data, exploits and zero-days are also available for sale. Large botnets are available for rent, and so are services such as ransomware-as-a-service and DDoS-as-a-service. DDoS attacks flood a target system with more traffic than it can handle, bringing it down.
There is a market for exploits, which are attacks on computer systems made through a particular vulnerability of the system, and for trading these exploits. There is a growing number of actors trading such exploits which drives up supply.
An iOS zero-day – an attack mechanism targeting previously unknown vulnerabilities in Apple mobile operating systems – can cost as much as US$1.5 million (S$2 million). It is no wonder that technically gifted programmers see the attraction of providing such services.
In 2018, we will see an increasing number of extortionist attacks around the world targeting critical infrastructure. Transportation, energy and medical institutions are choice targets as a service outage can cause severe public backlash and, therefore, increases the possibility of a payout.
In recent months, the healthcare industry has been a victim of more attacks. This is because of the value of healthcare data – such as medical histories – which can be used for a variety of cyber fraud.
Cyber attacks will cost US hospitals more than US$305 billion over five years and one in 13 patients will have their data compromised by a hack, according to industry consultancy Accenture in a 2015 report.
A 2016 study by Brookings showed that, since late 2009, the medical information of more than 155 million Americans has been exposed without their permission through about 1,500 breaches.
Healthcare institutions are vulnerable partly because government regulations forced healthcare operators to adopt electronic health records and other advances even if they weren’t ready to adequately invest in security.
Would-be smart nations should take note that mass adoptions of digital solutions do not create a security nightmare, giving hackers an endless attack surface to target.
EVOLVE TO STAY AHEAD
So how should organisations respond? For swift detection and mitigation of threats, round-the-clock monitoring of networks, applications and devices, through an in-house security operation centre or outsourced service, is critical. The next generation of security operations centres also need to incorporate big data analytics and deep machine learning capabilities to keep on top of the massive amount of data generated.
Organisations need to be more aggressive in vulnerability assessment and penetration testing by conducting them more frequently. They might even consider providing incentives to white hat hackers through bug bounty programmes (which pay these hackers for discovering flaws).
At the operational level, the overall incident response framework must be routinely audited and strengthened. The incident response team must be drilled through specific skills training, table top scenarios, and full-fledged red team-blue team exercises (blue team being the defenders; red team the simulated attackers), where they are pitted against a group of white hat hackers trying to break through their security. External assistance should be sought if there is a lack of internal skillsets or personnel.
Singapore organisations especially need to take the threat of cyber attacks more seriously. A survey conducted by managed security services provider Quann and research firm IDC in June last year covered 150 senior IT professionals from medium to large companies based in Singapore, Hong Kong and Malaysia.
The results showed that 40 per cent of the respondents do not have incident response plans for when they are being attacked and 67 per cent do not practise their incident response plans.
Cyber security requires a comprehensive approach that goes beyond the chief information security officer or head of information technology. The executive leadership must not see cyber security as a cost centre and an IT issue, but as an integral part of corporate risk management.
Senior management and the board must understand the threat landscape and data protection strategies.
Beyond the board and management, every employee matters. A Cyber Security Agency of Singapore 2017 survey showed that Singaporeans display risky behaviour that jeopardises their own and their company’s cyber security. It does not matter how advanced the corporate anti-virus is if employees indiscriminately download free but potentially malware-laden software from dubious sources. Every careless employee is an open door for hackers to exploit.
With the number and complexity of attacks rising, enterprises need to stay on top of their cyber security preparedness.
Effective cyber security is not about keeping up with the cyber security products arms race. Instead, it is about ensuring that seemingly mundane tasks, such as keeping patches up-to-date, ensuring that security hardware is maintained and managed well, and ensuring compliance with user policies and procedures, are performed well by human beings.
Even with the best technology, the human factor plays a critical role in ensuring enterprises stay cyber secure. Firewalls must be kept up-to-date but the most important firewall is still the human one.
•Foo Siang-tse is managing director of Quann, a managed security services provider. Shashi Jayakumar is head of the Centre of Excellence for National Security and Executive Coordinator, Future Issues and Technology at the S. Rajaratnam School of International Studies, Nanyang Technological University.
ANOTHER new year has dawned, and it’s time to preview what to expect in 2018.
The most obvious topic would be to anticipate how Donald Trump, the most unorthodox of American presidents, would continue to upset the world order. But more about that later.
Just as importantly as politics, we are now in the midst of several social trends that have important long-term effects. Some are on the verge of reaching a tipping point, where a trend becomes a critical and sometimes irreversible event. We may see some of that in 2018.
Who would have expected that 2017 would end with such an upsurge of the movement against sexual harassment? Like a tidal wave it swept away Hollywood producer Harvey Weinstein, film star Kevin Spacey, TV interviewer Charlie Rose and many other icons.
The #MeToo movement took years to gather steam, with the 1991 Anita Hill testimony against then US Supreme Court nominee Clarence Thomas being a trailblazer. It paved the way over many years for other women to speak up until the tipping point was reached. So, in 2018, expect the momentum to continue, and in more countries.
Another issue that has been brewing is the rapid growth and effects of digital technology. Those enjoying the benefits of the smartphone, Google search, WhatsApp, Uber and online shopping usually sing its praises.
But the “Fourth Industrial Revolution” is like Dr Jekyll and Mr Hyde. It has many benefits but also serious downsides, and the debate is now picking up.
First, automation with artificial intelligence can make many jobs redundant. Uber displaced taxis, and will soon displace its drivers with driver-less cars.
The global alarm over job losses is resonating at home. An International Labour Organisation report warning that 54% of jobs in Malaysia are at high risk of being displaced by technology in the next 20 years was cited by Khazanah Research Institute in its own study last April. TalentCorp has estimated that 43% of jobs in Malaysia may potentially be lost to automation.
Second is a recent chorus of warnings, including by some of digital technology’s creators, that addiction and frequent use of the smartphone are making humans less intelligent and socially deficient.
Third is the loss of privacy as personal data collected from Internet use is collected by tech companies like Facebook and sold to advertisers.
Fourth is the threat of cyber-fraud and cyber-warfare as data from hacked devices can be used to empty bank accounts, steal information from governments and companies, and as part of warfare.
Fifth is the worsening of inequality and the digital divide as those countries and people with little access to digital devices, including small businesses, will be left behind.
The usual response to these points is that people and governments must be prepared to get the benefits and counter the ill effects. For example, laid-off workers should be retrained, companies taught to use e-commerce, and a tax can be imposed on using robots (an idea supported by Bill Gates).
But the technologies are moving ahead faster than policy makers’ capacity to keep track and come up with policies and regulations. Expect this debate to move from conference rooms to the public arena in 2018, as more technologies are introduced and more effects become evident.
On climate change, scientists frustrated by the lack of action will continue to raise the alarm that the situation is far worse than earlier predicted.
In fact, the tipping point may well have been reached already. On Dec 20, the United Nations stated that the Arctic has been forever changed by the rapidly warming climate. The Arctic continued in 2017 to warm at double the rate of the global temperature increase, resulting in the loss of sea ice.
These past three years have been the warmest on record. The target of limiting temperature rise to 2°C above pre-industrial levels, a benchmark just two years ago by the UN’s top scientific climate panel and the Paris Agreement, seems outdated and a new target of 1.5°C could be adopted in 2018.
But it is much harder to meet this new target. Will political leaders and the public rise to the challenge, or will 2018 see a wider disconnect between what needs to be done, and a lack of the needed urgent response?
Another issue reaching tipping point is the continuing rise of antibiotic resistance, with bacteria mutating to render antibiotics increasingly ineffective to treat many diseases. There are global and national efforts to contain this crisis, but not enough, and there is little time left to act before millions die from once-treatable ailments.
Finally, back to Trump. His style and policies have been disruptive to the domestic and global order, but last year he seemed unconcerned about criticisms on this. So we can expect more of the same or even more shocking measures in 2018.
Opposition to his policies from foreign countries will not count for much. But there are many in the American establishment who consider him a threat to the American system.
Will 2018 see the opposition reach a tipping point to make a significant difference? It looks unlikely. But like many other things in 2018, nothing is reliably predictable.
Martin Khor is executive director of the South Centre. The views expressed here are entirely his own.
The robots are coming to the rescue. So screamed a headline about artificial intelligence. The article appeared recently in The Times of London and it reported that, just as automation and outsourcing will boost productivity growth, so will driverless cars. They’re reliable, safer and more efficient: what’s not to like?
But as I read about this dazzling prospect, it occurred to me: what will truck drivers do when driverless trucks replace them?
The conventional answer is that former truckies will do “retraining” courses. It’s more likely that many of them will be jobless and dissatisfied with their lot in life. Their numbers will grow, as will their discontent. What will happen then?
We live in an age in which digital technology is revolutionising our lives. Just think of cloud computing and big data analytics. Or the online auction giant eBay, the private car group Uber and the online streaming media group Netflix. All the global tech platforms – Facebook, Microsoft, Google, etc – are truly innovative. They wreak chaos across the business world. And they are reinventing the nature of society.
Some call the new era “disruption”, but I think “creative destruction” is a better descriptor. That’s the famous phrase used by Joseph Schumpeter (1883-1950) to describe capitalism.
By this, the leading Austrian-American economist meant that new products, technology or production methods would provoke change, forcing established companies to adapt quickly to a new environment or fail.
In Capitalism, Socialism and Democracy (1942), Schumpeter argued: “Capitalism … is by nature a form or method of economic change and not only never is but never can be stationary.” As a result, it depends on a process that constantly destroys old economic structures and creates new ones to meet new market demands.
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From today’s standpoint, Schumpeter offers sound insights into everything from the success of Apple and Amazon to the traumas of cabbies and cashiers; and his idea of “creative destruction” seems evident everywhere.
Taxis are the most visible example of a once-dominant industry struggling to adapt: by creating a smartphone rider app and lowering fares, Uber threatens cab cartels.
But the taxi industry is hardly alone. Online retailing hurts big shopping malls. Amazon means bookstores are in short supply. Streaming services are undoing the cable business. The online rental-lodging site Airbnb has made getting a room cheaper and more accessible. Sex robots could put female porn stars out of work!
However, Schumpeter also warned that capitalism is a doubled-edge sword. As prosperity increases, progress is taken for granted; and the disruption caused by “creative destruction” will become intolerable.
Capitalism is innovative and it is most successful when most innovative. It’s just that when capitalism is most innovative, it is also most destructive, sometimes visiting mayhem on certain groups.
Perhaps nowhere has digital technology played out more dramatically than across the media. Barriers to entry in broadcasting and publishing have tumbled. Cultural gatekeepers have been sidelined. Many more opinions are heard and read. And consumers are increasingly using their iPhones or iPads to follow news and entertainment.
The flipside is that public confidence in the press might continue to decline, especially if news becomes a toxic stew of opinion and gossip. That could undermine the Fourth Estate’s ability to hold institutions accountable. Add to this the mass redundancies across newsrooms, and it’s clear journalism has seen a lot of “destruction” recently.
The populist backlash against globalisation is also vindicating Schumpeter’s thesis. Virtually every western nation that undergoes a process of rapid economic change experiences a nativist reaction to the dislocation involved.
From the right to the left, Pauline Hanson, Winston Peters, Bernie Sanders, Jeremy Corbyn and all those European nationalists resonate with anxious, even hostile, parts of the electorate.
Indeed, it is a mistake to dismiss ordinary folks attracted to populist insurgencies as history’s leftovers. The digitally ditched are as modern as digital technology itself. And they look to the nation state for support.
However, there is a downside. When driverless trucks replace truck drivers, there will be a lot of irate people, who equate digital innovation with unemployment. As more and more manual and lower-wage workers are also displaced, the losers of “creative destruction” could help roil politics in a way that makes the Trump-voting angry white men from America’s old industrial heartland look tame.https://www.youtube.com/watch?v=wxDRqeuLNag It is hard to imagine a more brutal blow at the image of capitalism.
Tom Switzer is executive director of the Centre for Independent Studies and a presenter at the ABC’s Radio National.
Cyber warfare gets a lot of press these days—if it isn’t the Chinese stealing millions of U.S. Federal personnel records, it’s the Russians breaking into the Democratic National Headquarters email, the North Koreans hacking Sony or unnamed hackers cracking Uber’s servers. You can’t open a website or change the channel without hearing about more cyber horrors, but are we really picking up on the whole cyber warfare game? Is there more to it than hackers in dark rooms stealing passwords and cracking into Wall Street servers? I think there is. I think our major adversaries are playing the long game and have a plan to not only dominate the digital world, but to use our own technology—and freedoms—against us. And it’s about to get worse as the world switches from human controlled operations to autonomy. Drones will see the first wave of what I call autonomy warfare.
This new type of warfare is producing new types of superpowers. Just having nuclear weapons doesn’t get you into the new superpower club. Cyber capacity is the deciding factor. Countries with the best (and most) coders, the most extensive cyber production capability, the best cyber infrastructure and best regulatory environment are the new superpowers. Using these measures, the United States and People’s Republic of China are the sole cyber superpowers. True, the Commonwealth nations (particularly the UK and India), Western Europeans, Israelis and Russians are formidable cyber powers, but all lack the massive numbers of coders, computer engineers, available capital and, most importantly, cyber industrial capacity to be true cyber superpowers.
The United States remains the top superpower—for now. We became a cyber superpower much like we dominated the world back when military power and industrial capacity determined superpower status—by using American innovation to drive a free market economy largely free of government regulation. The fiercely competitive American market, not its government, drove nearly all the growth in America’s cyber power. True, the American military and intelligence community did make major breakthroughs—DARPA invented internet protocol messaging; up until the mid 1990s the world’s faster supercomputer title was a race between the U.S. National Security Agency and the UK’s Government Communications Headquarters (the two also contest who invented the computer). However, the free market left the government in the dust starting in the early 1980s. Mainframes were too bulky for American business, so Intel invented the microprocessor, Microsoft invented the operating system and IBM invented the personal computer. The internet may have worked for DARPA, but normal people could never access it until Mosaic improved on Sir Tim Berners-Lee’s “world wide web browser” and Cisco manufactured MIT’s “internet router” in massive numbers. Internet signals flashed over fiber optics invented by Corning Glass (based on British research). When cables became an encumbrance, internet traffic switched to Motorola’s “cellular phone” and then to “WiFi” built by AT&T (based on Australian research—I’m seeing a pattern here…).
What America didn’t do as they invented modern computing was to consider security from the start when developing these systems. To this day, Silicon Valley has successfully resisted most regulation, however sensible. As a result, we have an Internet that is impossible to secure and social media apps like Facebook that Russian intelligence can easily manipulate to spread propaganda.
How China Became a Superpower
American cyber achievements are impressive, but China started to catch up when cheap Chinese labor attracted the bulk of America’s cyber production capacity. China took a radically different approach from America in becoming a cyber superpower. Whereas the U.S. let the market guide cyber development, China used government guidance supported by their intelligence community to leapfrog ahead by outright stealing code, convincing American companies to manufacturer in China, strategically acquiring American cyber companies and using America’s own massive university system to train their computer scientists and engineers. Unlike America’s market based system, most moves made by China are government directed and supported by the full power of the state. China has done an impressive job of catching up with America in a short time.
China actively uses America’s free market system against the United States. Few American students could attend Chinese universities for cyber education (assuming they’d want to). According to the Pew Research Center, 57 percent of doctoral degrees in engineering and 53 percent of doctoral degrees in computer and information sciences went to foreigners in 2012-13. More than half of these foreign students were Chinese. Few countries on earth would have permitted the bulk of their chip and memory manufacturing to move to a foreign country (let alone an adversary country) because production costs were lower, but America did. Intel Corp is the last remaining company that has major chip manufacturing in America. The United States could never direct an American company to buy a foreign company to acquire technology to advance the American cyber industrial base, but China does it routinely. Remember that IBM personal computer invention I mentioned earlier? The Chinese company Lenovo now owns it.
The Road to the Cyber Wars
We’re about to see a replay of the early 21st Century cyber wars as the world switches to autonomous systems and drones will be the first battlefield. Again, this is a technology pioneered by the United States (and its close ally, Israel). Israel developed the first modern drone, the Scout, in the late 1970s and used it to massacre the Syrian Air Force over the Bekka Valley in 1983. A dual Israeli/American citizen developed the first beyond line of sight drone, the MQ-1, in the late 1990s and the U.S. Air Force used it to gut Al Qaida after 9/11. Northrup Grumman made the first nearly autonomous drone, the RQ-4 Global Hawk, in the early 2000s.
What America did NOT do is dominate the consumer and commercial drone market, largely because of their failure to provide sensible civil drone regulations. That hasn’t stopped China from dominating the limited markets where American regulations allow drones to fly. China’s DJI commands between 70 and 90% of the consumer/prosumer market in the U.S. China’s Yuneec is a distant second followed by France’s Parrot (all of which are manufactured in China). China is equally active in shaping the development of American drone regulations and standards. DJI is the co-chair of the FAA’s Drone Advisory Council and has been invited to every FAA drone aviation rulemaking committee convened so far. DJI is a reliable and active member of every major ASTM and RTCA drone standards committee. DJI also uses its considerable market influence to shape the American unmanned traffic management (UTM) system. American UTM providers know they can’t risk alienating DJI or they risk getting cut off from DJI’s systems.
If you think all this activity is merely economic, I suggest you re-read the paragraphs above on cyber warfare. Penetrating every aspect of America’s first autonomous systems is probably a major goal of the Chinese and a first shot in the autonomy wars.
The Threat to Security
Autonomous systems implemented badly are a major security risk to any country. Unlike manned systems, there are no humans in the loop and autonomous systems can be hacked and repurposed to do relatively passive tasks like espionage or active tasks like purposefully crashing. To use an example before the autonomous age, if the Nazis wanted to steal the RAF’s Spitfire design and manufacture it, they would have to steal hundreds of paper blueprints, spend months retooling factory presses and years manufacturing their ill-gotten analog aircraft. Pilot training would have taken years. Autonomous aircraft designs exist as CAD drawings that can be instantly transferred to manufacturing robots or 3-D printers. Pilot training isn’t an issue. Simply steal flight algorithms while hacking the CAD drawings. A modern adversary can hack their way into a first class autonomous Air Force in months or weeks—not years.
Or adversaries can cause havoc by re-purposing existing autonomous military and commercial drones. Autonomous drones are particularly vulnerable to autonomy warfare. Drones require a data link to function and their ground control stations are often connected to the Internet directly. Most commercial drones will connect with a UTM, introducing another attack vector for autonomy warfare. The Americans are once again minimally regulating drones to allow market forces maximum flexibility to drive innovation. Drones less than 55 lbs don’t require airworthiness standards and standards for larger drones are still in their infancy. America appears poised to repeat their mistakes from the early age of cyber warfare.
There is, however, considerable hope for drone security at the dawn of the autonomy age. There are large numbers of consumer drones in the U.S., but not large numbers of commercial drones—yet. The FAA decided not to impose security requirements on drones less than 55 lbs flown within visual line of sight, but they haven’t decided how to regulate large drones or small drones that fly beyond line of sight. UTM is in its infancy and it’s not too late to add viable security standards. Perhaps most importantly, the FAA hasn’t issued regulatory guidance for UAS remote identification, operations over people, beyond line of sight, large UAS or even autonomous operations themselves. There’s still time to put their foot down and recognize that industry will resist security regulation. There’s still time to recognize that China is playing the long game and will do what they can to shape our regulations to make it easier for them to win at autonomy warfare.
Will the FAA step up and recognize that cyber security is key to aviation safety in the autonomy age? Will they stand up to industry and write sensible security standards into airworthiness standards and regulations? They didn’t hesitate to dictate bird impact standards to airline manufacturers. Will they do the same for a much, much larger threat to aviation safety?
Before the 2016 presidential campaign, David Carroll, a media professor at New York’s Parsons School of Design, didn’t know much, if anything, about Cambridge Analytica. Despite studying data collection and privacy, he says he had probably only heard the name of the data analytics company mentioned once or twice. But that was before the election. And it was before, of course, it became clear that the firm—partially owned by Trump mega-donor Robert Mercer and the place where former White House Chief Strategist Steve Bannon once served as vice president—would help propel Donald Trump into the White House by cultivating vast troves of information on an untold number of American voters to craft controversial and highly targeted political messages.
But even still, it wasn’t until a few months after the election that the alarm bells started going off for Carroll. Paul-Olivier Dehaye, the co-founder of PersonalData.IO, a startup that helps individuals request their data from companies like Tinder, Uber, and Facebook, Carroll says, told him he suspected that Cambridge Analytica, with offices around the world, may have processed the data of American voters in 2016 in the UK. While the company’s tactics were a complete mystery, if that were true, Carroll, an American, could request what information it had on him as allowed by British data protection laws. So in early 2017, the two set out to pull back the curtain on the data tactics of Cambridge Analytica.
“He was curious,” Carroll, a self-described “data nerd,” tells Mother Jones. “I was curious. It was purely academic curiosity.”
Just a few months later, Carroll would find himself in the midst of a landmark data privacy legal battle.
Carroll’s quest started in February, when he formally requested his personal data from Cambridge Analytica, not knowing what, if anything, the company would give him.
At that point, the practices of Cambridge Analytica and its connection to both Mercer and Bannon were only starting to come under the microscope—questions that have since expanded to include the company’s possible connection to Russia’s social-media meddling in the election and, more recently, its potential collaboration with Wikileaks. As the Daily Beastreported in late October, last year Cambridge Analytica CEO Alexander Nix offeredWikiLeaks founder Julian Assange assistance in the release of 33,000 of former Secretary of State Hillary Clinton’s stolen emails. (Recently Nix said, “We did not work with Russia in this election, and moreover we would never work with a third-party state actor in another country’s campaign.”)
Just a month after filing the initial request, Carroll, to his surprise, received a letter signed by a chairman of London-based “behavioral research and strategic communication” firm SCL, the parent company to Cambridge Analytica, with a file of his personal data, including a set of political predictions about Carroll made by the firm. It rated Carroll a “very unlikely Republican” (this is true; Carroll voted for Democrats in the 2016 general and primary elections) and assigned him scores on various political issues: He scored a 3/10 on “gun importance,” a 7/10 on “national security importance,” and a 9/10 on “traditional social and moral values.” He tweeted, “I’d rank this somewhat differently but feels roughly accurate. Could be worse.”
7/ Here is CA/SCL voter issue profile/propensity model on me. I’d rank this somewhat differently but feels roughly accurate. Could be worse. pic.twitter.com/n4x3IRQrL8
The results were unsettling for Carroll and also for his thousands of Twitter followers, who he had been updating on his data-request efforts. It turned out a wide expanse of personal information about Carroll’s behavior was being connected to his voter file and shared with “commercial entities,” “research partners,” “political campaigns,” and other groups, according to the letter he received. “People were kind of terrified that this information was accurate,” Carroll says. “People had a visceral reaction that their voter files aren’t being protected like they ought to be.” While some of his followers said what he got was “typical data for the industry” or “no big surprise,” others called it “scary” and “deeply disturbing.”
But what was particularly problematic for Carroll was that, he believes, the profile the company sent him wasn’t nearly comprehensive. Nix and other Cambridge Analytica executives have boasted that the company has up to a startling 5,000 data points on each of the 230 million voters in the US. What Carroll received in March, according to his tweet at the time, was about 200 data points, and, even then, it wasn’t clear how or where the company got the data or who it was shared with, beyond the vague descriptions in the letter.
What’s more, the response came from someone at a British company, SCL, which suggested to Carroll that his data, and presumably the rest of Americans’ data, was in fact processed in the UK, just as Dehaye thought. And if the data had been processed in the US, Carroll suggests, there would be little incentive for them to share it given the restrictive data laws in America.
But according to the 1998 British Data Protection Act, any company that receives a personal data request is required to provide a “description of the personal data,” state their purpose of processing it, and disclose any people and countries, outside Europe, the data were shared with. A company that fails to comply with those standards, according to the law, is “guilty of an offense.” “As soon as I posted [SCL’s response] to Twitter,” Carroll says, “British academics started saying, ‘Hey, that’s illegal.’” Carroll argues that Cambridge Analytica failed to share the necessary information when he asked. To get the rest of his data—if there was in fact more, as Nix had bragged—Carroll would have to sue.
In April, Carroll and a group of an unspecified number of Americans who have remained anonymous to protect their privacy hired a British solicitor recommended by Dehaye, Ravi Naik, to launch the first-of-its-kind legal battle against the company.
This case, the group hopes, will clarify the legal requirements for British data-collecting companies—including those with information on non-European citizens. More specifically, the Data Protection Act also states that companies must obtain “explicit consent” from individuals before processing sensitive personal data, including “political opinions.” Cambridge Analytica, Naik argues, failed to obtain consent from American voters in 2016. “What the European regulations on data protection make clear is that if you want to collect and process sensitive personal data here, you should get consent to do so,” Naik tells Mother Jones. “Political opinions are recognized as a class of sensitive personal data, as information deserving of higher protection.”
American laws are much less forgiving. “If a [British] company has information about you, you have the right to access it, and if you ask for it, they have to give it to you,” Carroll tells Mother Jones. “We don’t have that right in the United States.” In the US, companies don’t need consent to collect its citizens’ data and aren’t legally obligated to share it with them. In fact, Carroll wouldn’t even have a case if the company processed its data in the United States (and won’t, if that turns out to be true). As Naik told the Guardian earlier this year, “It’s this fascinating situation because when it became apparent that Cambridge Analytica had processed Americans’ data in Britain, it suddenly opened up this window of opportunity. In the US, Americans have almost no rights over their data whatsoever, but the data protection framework is set up in such a way that it doesn’t matter where people are: it matters where the data is processed.”
The result of this case could blow the lid off how private data was used to shape votes and the outcome of 2016 election—and how it might be used in the future. As University of Maryland law professor and big data expert Frank Pasquale told the Guardian, “I think [this case] will be the model for other citizens’ actions against other big corporations. I think we will look back and see it as a really significant case in terms of the future of algorithmic accountability and data protection.”
Cambridge Analytica opened its doors in 2013 and claims to use big data to predict human behavior and influence political elections, according to the company’s website. But what sets Cambridge Analytica apart from other data firms is that it claims to use what’s known as psychographics to build its voter profiles. Many political campaigns have used demographics (e.g., age, race, gender) to target political messaging, and President Obama successfully and famously used consumer data to target voters. But psychographics, in theory, go deeper, claiming to be able to predict a voter’s personality traits, such as how organized, extroverted, or quick to worry they are, by looking at a person’s online and consumer behavior. Cambridge Analytica is the only data firm, Republican or Democratic, that has publicly claimed to use psychographics in political campaigns. All this begs the question: How does Cambridge Analytica then connect up to 5,000 data points of consumer behavior with American voter files to build their profiles?
“People don’t realize that all of their consumer behavior—every time they swipe their credit card, what websites they visit, the TV shows they watch—is being re-connected to your voter file and processed internationally,” Carroll says. “And we can’t opt out of it.”
Nix, however, offered a glimpse into the company’s mysterious practices in a presentation he gave nearly two months before Election Day 2016. In it, he claims that his company had been a powerful shaper of public opinion during the 2016 Republican primary campaign, bragging that Cambridge Analytica helped Ted Cruz rise from being “one of the less-popular” candidates to being the “second-most threatening contender.” Cruz, Nix said, accomplished this in part by sending highly-targeted, personalized political messages over social media and television to voters in key states—the same controversial tactics that, after Cruz dropped out of the race, were supposedlydeployed on behalf of the election’s winner.
Nix then gives an example of those tactics: “The Second Amendment might be a popular issue amongst the electorate,” he said. “For a highly neurotic and conscientious audience, you’re going to need a message that is rational, and fear-based, or emotionally based.” He said, gesturing to his presentation slides, which displayed a photograph of a gloved hand breaking a window above the text, The Second Amendment isn’t just a right. It’s an insurance policy. DEFEND THE RIGHT TO BEAR ARMS. “In this case, the threat of a burglary and the insurance policy of a gun is very persuasive.”
What’s particularly frightening about Cambridge Analytica and SCL, Molly McKew, an information warfare expert and specialist on Russia-US relations notes, is that they are operating internationally to supposedly influence elections outside of where they are operating, in the US and elsewhere.
“Nobody wants to believe that information, coming from some place they don’t really understand, could change how they think or what their decisions are, but it can—for any of us,” McKew tells Mother Jones. “Why does some company incorporated in the United Kingdom have [our data]? What the hell is that for? If it were just about selling shoes, or getting you to buy vitamins or whatever crap—ok, fine. But that’s not what it’s being used for, and they specifically say that. [SCL] is a company that’s marketing themselves as a military-grade psychological warfare and psychological operations company. That is a problem for all of us.”
To be sure, there is another way to read the secrecy shrouding Cambridge Analytica’s data collection efforts. Daniel Castleman, co-founder of progressive data firm Clarity Campaign Labs, which worked on the Obama for America campaign, tells Mother Jones that the role of psychographics in the 2016 election has almost certainly been “overplayed.” There isn’t even a consensus in the data community on if these tactics work, he notes: “While how [psychographics] are described gives the impression that they help campaigns give deep insights in the minds of voters, current implementations have questionable scientific legitimacy and often lack accuracy when constructed from available voter and consumer data.”
And since the election, even the Trump campaign has downplayed the role of Cambridge Analytica’s tactics in helping it win. “We as a campaign made the choice to rely on the voter data of the Republican National Committee to help elect President Donald J. Trump,” Trump campaign executive director, Michael Glassner, said in a statement in October. “Any claims that voter data from any other source played a key role in the victory are false.” (Though of course there could be a certain amount of self-preservation in this statement, which came a few hours after the Daily Beastreported Nix’s offer to help Assange in the release of Clinton’s stolen emails.)
To add, even Cambridge Analytica itself is sending mixed messages about the use of psychographics on the Trump campaign. Nix said in that same fall 2016 presentation, “Of the two candidates left in the election, one of them is using these technologies,” referring to Trump. Then, at a December post-election panel hosted by Google, Matt Oczkowski, Cambridge’s head of product, said “I don’t want to break your heart; but we actually didn’t really do any psychographics with the Trump campaign.”
Carroll plans to file a formal lawsuit against the firm early next year, and the group is currently raising funds to cover any costs in case of a loss (in the UK, if they lose, they have to pay for the other side’s legal fees).
Cambridge Analytica maintains that they haven’t broken any laws and seems ready for a fight. “Mr. Carroll’s claims are unfounded, and unfortunately, he is wasting other people’s money with this spurious legal action,” Cambridge Analytica tells Mother Jones in an email. “Cambridge Analytica abides by all relevant data protection laws and, just as importantly, the company’s core values of integrity, respect and honesty. Data privacy is a fundamental right and one that Cambridge Analytica takes very seriously.”
In response to Carroll’s specific allegations against them, Cambridge Analytica tells Mother Jones, “Given that this is a legal matter, it would not be appropriate to make any further comment.” They also note, “Cambridge Analytica is not a data miner or a data broker. We can help clients extract value from their own data, and do our own market research, before exploring to what extent other data is available.”
The group of plaintiffs meanwhile steadfastly believes that their data are possibly being used to “manipulate our democracy, without our knowledge or consent,” according to their CrowdJustice fundraising campaign. A favorable ruling for the group would not just mean access to their own data and a chance to opt-out of the company’s monitoring, but it would also provide information on how the company is targeting voters—and could target voters in future elections. Armed with this knowledge, voters would in theory be less persuadable, and could even choose to pull their data from the hands of Cambridge Analytica and its peers.
“If David wins, everyone wins,” an optimistic Naik says.
2017 has been a year filled with major ups and downs in the digital world. While technology seems to be rocketing sky-high with various kinds of smartphones and smart-cars, it does not mean that these are happy times. This year has shown us how the same technology that gives us so much, takes away more than what we bargained for- our privacy. Therefore, we bring you the list of the biggest hacks of 2017 to make you aware of how technology not only gives power, but also cripples us.
This year has shown us how the same technology that gives us so much, takes away more than what we bargained for- our privacy.
List of the Biggest Hacks of 2017
The group: Shadow Brokers is a group that leaks gigabytes of NSA’s weaponized software exploits.
What was compromised? 300MB of materials was stolen from the National Security Agency (NSA). Leaks included vulnerabilities of Windows OS, along with Windows 8, Windows 2012, and a tool named Fuzzbunch that loads binaries into targeted networks.
Is it very bad? According to the Independent security experts, this particular data breach was the most damaging release of Shadow Brokers.
The Virus: This ransomware was used to lock down an infected computer’s files. People were asked to pay a ransom in order to get access to their files and folders.
How many victims? More than 200,000 people over 150 countries were victims.
Compromised places: Ukraine, Taiwan, and Russia. Apart from them, hospitals in U.K., universities in China and international firms like FedEx were also victim to this attack.
WikiLeaks Vault 7
What’s WikiLeaks? WikiLeaks is a “not-for-profit media organization,” launched for the purposes of distributing original documents from anonymous sources.
So what’s Vault 7? It’s a string of documents that gives a detail description about the activities of the CIA and their plans on electric surveillance and cyber warfare.
And what’s gonna be compromised? Web browsers that include Google Chrome, Microsoft Edge, Opera Software ASA, and Mozilla Firefox. Apart from that there are cars, smart TVs, and smartphones that work on iOS, Android and Microsoft which they might make use of.
What’s the Bug all about? It’s a security bug that affected the reverse proxies of Cloudflare. It was used to run past the buffer and retract memory containing private information.
What kind of data could be obtained? Personal information on the HTTP cookies, HTTP POST bodies, authentication tokens, and more of such sensitive data.
What’s more? The customer’s data was leaked out and would go to another Cloudfare customer who was in the server’s memory. Some data was also cached by a few search engines.
198 Million Voters record exposed
Wait, what? Around 198 million people’s voter records were stolen which included personal information as well as the voter’s profiling data.
Where was the Data stored? The data of the U.S. voters was stored in an Amazon S3 storage server called Deep Root Analytics which is owned by a Republican data analytics firm.
How big is it? It is considered to be the largest known exposure of voters information until now.
Freedom Hosting II
What’s the hack? With a single hack, around 20 per cent of the websites on the dark web were taken offline alongside the responsible publishing details of the administrators.
What’s the reason? The attack took place after one hacker claimed that a lot of the hacked websites hosted child pornography.
Imgur data breach
So what’s Imgur? Imgur is a popular image-sharing app. The data breach took place in 2014, but the reports were made public this year.
How big was it? The site revealed that around 1.7 million email addresses and passwords were lost by them in the breach.
Is it scary? Nope. It should be less of a concern because the site only collects mails ids and passwords. The statement suggested that the hackers could have decrypted the stolen credentials using brute force attack.
What’s Uber got into now? In October 2016, Uber’s data got hacked. Uber kept it under wraps until finally this November, they came out in the public.
How bad was it? Personal data of around 57 million customers and drivers was stolen.
Whoa! What’s more? Uber paid around $100,000 to the hackers to delete and get rid of the stolen data. They also requested them not to make it open to the public.
What’s the company about? The company is an accounting giant which has very influential and wealthy clients.
Who were caught in the loop? The U.S. Dept. of state, energy, defence, and homeland security, National Institutes of Health, U.S. Postal Service, Fifa, 3 airlines, 4 global banks, energy giants, pharmaceutical companies, car manufacturers, Deloitte’s U.S. staff and their communication with the clients, etc. Phew!
How did the hackers get so much? The hackers used the administrator’s account which gave them access to their complete list of database.
Is it bad? Equifax is a consumer credit reporting agency that collects information of around 800 million individual consumers and millions of businesses worldwide.
What got stolen? It has been said that this is the worst data breach in the history of United States. Half of the U.S population’s Social Security numbers were stolen by the attackers.
That’s huge! It is being referred to as the mother of all hacks.
What’s DaFont? It’s a well-known source for free fonts on the web. It offers 32,000 fonts for free.
Let’s talk numbers! A total of 699,464 users’ accounts and passwords were stolen. The hacker was able to crack over 98 per cent of the passwords. The breach was carried out by an unknown hacker.
How did he do it? He said that it was easy to get access as he made use of a ‘union-based SQL injection vulnerability in their software and cracked the hashed passwords, which were encrypted by deprecated MD5 algorithm.
And although the list goes on and on, we stop here by giving you the biggest hacks of 2017 that not only shook a country but ended up affecting the world as a whole. Nothing seems to be personal or private once a person starts making use of the internet. This year has shown us how technology takes away more from us than what it gives us in return.