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Can Police control your self-driving car?

In 2009 GM equipped 17,000 of its units with “remote ignition block,” a kill switch that can turn off the engine if the car is stolen. But that was just the beginning.

Imagine this: You’re leaving work, walking to your car, and you find an empty parking spot — someone stole your brand new Tesla (or whatever fancy autonomous car you’re driving). When you call the police, they ask your permission for a “takeover,” which you promptly give them. Next thing you know, your car is driving itself to the nearest police station. And here’s the kicker — if the thief is inside he will remain locked inside until police can arrest them.

This futuristic and almost slapstick scenario is closer than we think, says Chief Innovation Officer Hans Schönfeld who works for the Dutch police. Currently, his team has already done several experiments to test the crime-halting possibilities of autonomous cars. “We wanted to know if we can make them stop or drive them to certain locations,” Schönfeld tells me. “And the result is: yes, we probably can.”

The Dutch police tested Tesla, Audi, Mercedes, and Toyota vehicles, he reports, adding “We do this in collaboration with these car companies because this information is valuable to them, too.

“If we can hack into their cars, others can as well.”

Companies ‘can sack workers for refusing to use fingerprint scanners’

Businesses using fingerprint scanners to monitor their workforce can legally sack employees who refuse to hand over biometric information on privacy grounds, the Fair Work Commission has ruled.

The ruling, which will be appealed, was made in the case of Jeremy Lee, a Queensland sawmill worker who refused to comply with a new fingerprint scanning policy introduced at his work in Imbil, north of the Sunshine Coast, late last year.

Fingerprint scanning was used to monitor the clock-on and clock-off times of about 150 sawmill workers at two sites and was preferred to swipe cards because it prevented workers from fraudulently signing in on behalf of their colleagues to mask absences.

The company, Superior Woods, had no privacy policy covering workers and failed to comply with a requirement to properly notify individuals about how and why their data was being collected and used. The biometric data was stored on servers located off-site, in space leased from a third party.

Lee argued the business had never sought its workers’ consent to use fingerprint scanning, and feared his biometric data would be accessed by unknown groups and individuals.

“I am unwilling to consent to have my fingerprints scanned because I regard my biometric data as personal and private,” Lee wrote to his employer last November.

“Information technology companies gather as much information/data on people as they can.

“Whether they admit to it or not. (See Edward Snowden) Such information is used as currency between corporations.”

Lee was neither antagonistic or belligerent in his refusals, according to evidence before the commission. He simply declined to have his fingerprints scanned and continued using a physical sign-in booklet to record his attendance.

He had not missed a shift in more than three years.

The employer warned him about his stance repeatedly, and claimed the fingerprint scanner did not actually record a fingerprint, but rather “a set of data measurements which is processed via an algorithm”. The employer told Lee there was no way the data could be “converted or used as a finger print”, and would only be used to link to his payroll number to his clock-on and clock-off time. It said the fingerprint scanners were also needed for workplace safety, to accurately identify which workers were on site in the event of an accident.

Lee was given a final warning in January, and responded that he valued his job a “great deal” and wanted to find an alternative way to record his attendance.

“I would love to continue to work for Superior Wood as it is a good, reliable place to work,” he wrote to his employer. “However, I do not consent to my biometric data being taken. The reason for writing this letter is to impress upon you that I am in earnest and hope there is a way we can negotiate a satisfactory outcome.”

Lee was sacked in February, and lodged an unfair dismissal claim in the Fair Work Commission.

He argued he was sacked for failing to comply with an unreasonable direction, because the fingerprint scanning was in breach of Australian privacy laws. His biometric information was sent to a separate corporate entity that was not his employer, Lee argued. His employer had no privacy policy in place at the time, and he argued it had failed to issue a privacy collection notice to its employees, as required by law. Lee argued the company had effectively breached the privacy of its 150 workers twice a day, every day since fingerprint scanning was introduced.

But the unfair dismissal claim failed. The Fair Work Commission found the site attendance policy that Lee had breached was lawful. It found that although the company may have breached privacy laws, the site-attendance policy was not automatically rendered unlawful as it related to Lee.

“While there may have been a breach of the Privacy Act relevant to the notice given to employees, the private and sensitive information was not collected and would never be collected relevant to Mr Lee because of his steadfast refusal,” the commission found. “The policy itself is not unlawful, simply the manner in which the employer went about trying to obtain consent may have constituted a breach of the Privacy Act.”

Lee told Guardian Australia he planned to appeal. He said the ruling implied that Australians only owned their biometric data until an employer demanded it, at which point they could be sacked if they refused to consent.

“My biometric data is inherently mine and inseparable from me,” Lee said. “My employer can’t demand it or sack me for refusing to give it.”

“It’s not about this particular employer. Ownership to me means that I can refuse consent without being sacked.”

NBCUniversal Taps Machine Learning to Tie Ads to Relevant Moments on TV

NBCUniversal announced a new machine learning tool today that helps brands place ads around scenes relevant to their product across any of the media giant’s broadcast and cable properties. The Contextual Intelligence Platform analyzes programming scripts, closed captioning data and visual descriptors of both ads and shows to find opportune moments for a given advertiser to appear as well as an emotional gauge for each scene determined by proprietary algorithms.

Focus groups for ads placed with the platform thus far have shown an average bump of 19 percent in brand memorability, 13 percent in likability and 64 percent in message memorability, according to Josh Feldman, vp and head of marketing and advertising creative, NBCU. The announcement comes as linear television providers continue to grapple with how to bring digital targeting practices to a medium that still largely operates on traditional phone-call media buying and manual ad placements. NBCU is now working with three to five advertisers for the system’s beta-test, and is aiming for an official release date early next year.

My devices are sending and receiving data every two seconds, sometimes even when I sleep

blockquote>When I decided to record every time my phone or laptop contacted a server on the internet, I knew I’d get a lot of data, but I honestly didn’t think it would reveal nearly 300,000 requests in a single week.

On average, that’s about one request every two seconds.

Are your devices sending and receiving data when you’re not using them?

They sure are. The quietest times fall — predictably — overnight. But even while I’m sleeping my devices are pretty busy talking to various companies. For example, here are the 841 times my devices made contact with 46 different domains between 10pm and 6:30am on the second night of the experiment. Most of these requests are background updates for things like my email and calendar or synchronisation that various apps like Dropbox or iCloud perform.

But exactly what each of them is doing is quite difficult to tell.

“Influencers” Are Being Paid Big Sums To Pitch Products and Thrash Rivals on Instagram and YouTube

“Influencers” are being paid big sums to pitch products on Instagram and YouTube. If you’re trying to grow a product on social media, you either fork over cash or pay in another way. This is the murky world of influencing, reports Wired. Brands will pay influencers to position products on their desks, behind them, or anywhere else they can subtly appear on screen. Payouts increase if an influencer tags a brand in a post or includes a link, but silent endorsements are often preferred.

Marketers of literature, wellness, fashion, entertainment, and other wares are all hooked on influencers. As brands have warmed to social-media advertising, influencer marketing has grown into a multibillion-dollar industry. Unlike traditional television or print ads, influencers have dedicated niche followings who take their word as gospel.

There’s another plus: Many users don’t view influencers as paid endorsers or salespeople—even though a significant percentage are—but as trusted experts, friends, and “real” people. This perceived authenticity is part of why brands shell out so much cash in exchange for a brief appearance in your Instagram feed.

Digital India: Government Hands Out Free Phones to Win Votes

In the state of Chhattisgarh, the chief minister, Raman Singh, has promised a smartphone in every home — and he is using the government-issued devices to reach voters as he campaigns in legislative elections that conclude on Tuesday.

The phones are the latest twist in digital campaigning by the B.J.P., which controls the national and state government and is deft at using tools like WhatsApp groups and Facebook posts to influence voters. The B.J.P. government in Rajasthan, which holds state elections next month, is also subsidizing phones and data plans for residents, and party leaders are considering extending the model to other states.

French Officer Caught Selling Access To State Surveillance Systems

A French police officer has been charged and arrested last week for selling confidential data on the dark web in exchange for Bitcoin,” reports ZDNet. French authorities caught him after they took down the “Black Hand” dark web marketplace. Sifting through the marketplace data, they found French police documents sold on the site. All the documents had unique identifiers, which they used to track down the French police officer who was selling the data under the name of Haurus.

Besides selling access to official docs, they also found he ran a service to track the location of mobile devices based on a supplied phone number. He advertised the system as a way to track spouses or members of competing criminal gangs. Investigators believe Haurus was using the French police resources designed with the intention to track criminals for this service. He also advertised a service that told buyers if they were tracked by French police and what information officers had on them.

Fake fingerprints can imitate real ones in biometric systems

Researchers have used a neural network to generate artificial fingerprints that work as a “master key” for biometric identification systems and prove fake fingerprints can be created.

According to a paper presented at a security conference in Los Angeles, the artificially generated fingerprints, dubbed “DeepMasterPrints” by the researchers from New York University, were able to imitate more than one in five fingerprints in a biometric system that should only have an error rate of one in a thousand.

The researchers, led by NYU’s Philip Bontrager, say that “the underlying method is likely to have broad applications in fingerprint security as well as fingerprint synthesis.” As with much security research, demonstrating flaws in existing authentication systems is considered to be an important part of developing more secure replacements in the future.

In order to work, the DeepMasterPrints take advantage of two properties of fingerprint-based authentication systems. The first is that, for ergonomic reasons, most fingerprint readers do not read the entire finger at once, instead imaging whichever part of the finger touches the scanner.

Crucially, such systems do not blend all the partial images in order to compare the full finger against a full record; instead, they simply compare the partial scan against the partial records. That means that an attacker has to match just one of tens or hundreds of saved partial fingerprint in order to be granted access.

The second is that some features of fingerprints are more common than others. That means that a fake print that contains a lot of very common features is more likely to match with other fingerprints than pure chance would suggest.

Based on those insights, the researchers used a common machine learning technique, called a generative adversarial network, to artificially create new fingerprints that matched as many partial fingerprints as possible.

The neural network not only allowed them to create multiple fingerprint images, it also created fakes which look convincingly like a real fingerprint to a human eye – an improvement on a previous technique, which created jagged, right-angled fingerprints that would fool a scanner but not a visual inspection.

They compare the method to a “dictionary attack” against passwords, where a hacker runs a pre-generated list of common passwords against a security system.

Such attacks may not be able to break into any specific account, but when used against accounts at scale, they generate enough successes to be worth the effort.

Facebook Filed A Patent To Predict Your Household’s Demographics Based On Family Photos

Facebook has submitted a patent application for technology that would predict who your family and other household members are, based on images and captions posted to Facebook, as well as your device information, like shared IP addresses. The application, titled “Predicting household demographics based on image data,” was originally filed May 10, 2017, and made public today.

The system Facebook proposes in its patent application would use facial recognition and learning models trained to understand text to help Facebook better understand whom you live with and interact with most. The technology described in the patent looks for clues in your profile pictures on Facebook and Instagram, as well as photos of you that you or your friends post.

It would note the people identified in a photo, and how frequently the people are included in your pictures. Then, it would assess information from comments on the photos, captions, or tags (#family, #mom, #kids) — anything that indicates whether someone is a husband, daughter, cousin, etc. — to predict what your family/household actually looks like. According to the patent application, Facebook’s prediction models would also analyze “messaging history, past tagging history, [and] web browsing history” to see if multiple people share IP addresses (a unique identifier for every internet network).

Dutch Government Report Says Microsoft Office Telemetry Collection Breaks EU GDPR Laws

Microsoft broke Euro privacy rules by carrying out the “large scale and covert” gathering of private data through its Office apps, according to a report commissioned by the Dutch government.

It was found that Microsoft was collecting telemetry and other content from its Office applications, including email titles and sentences where translation or spellchecker was used, and secretly storing the data on systems in the United States.

Those actions break Europe’s new GDPR privacy safeguards, it is claimed, and may put Microsoft on the hook for potentially tens of millions of dollars in fines. The Dutch authorities are working with the corporation to fix the situation, and are using the threat of a fine as a stick to make it happen.

The investigation was jumpstarted by the fact that Microsoft doesn’t publicly reveal what information it gathers on users and doesn’t provide an option for turning off diagnostic and telemetry data sent by its Office software to the company as a way of monitoring how well it is functioning and identifying any software issues.

Algorithms viewed as ‘unfair’ by consumers

The US-based Pew Research Center has found the American public is growing increasingly distrustful of the use of computer algorithms in a variety of sectors, including finance, media and the justice system.

report released over the weekend found that a broad section of those surveyed feel that computer programs will always reflect some level of human bias, that they might violate privacy, fail to capture the nuance of human complexity or simply be unfair.

Scope creep with Australia metadata retention

Telecommunications industry group Communications Alliance has revealed details of dozens of state and federal departments and agencies it claims are accessing so-called communications ‘metadata’.

The 2015 legislation that introduced the data retention regime authorised a list of “criminal law-enforcement agencies” to obtain warrant-free access to metadata. Those agencies included federal, state and territory police agencies, a number of anti-corruption bodies, Border Force, the Australian Securities and Investments Commission; and the Australian Competition and Consumer Commission.

However, last month at the hearing of an inquiry into the government’s bill aimed at enhancing police access to encrypted communications services, Communications Alliance CEO John Stanton said that a significantly larger number of organisations were accessing information kept by telcos to meet their data retention obligations.

In addition to police agencies and other organisations listed in the data retention legislation, it includes Centrelink, the Australian Taxation Office, Australia Post’s Corporate Security Group, Workplace Health and Safety, Work Safe Victoria, the Taxi Services Commission and a number of local councils.

Australia’s near-real-time facial recognition system, chilling effects

Civil rights groups have warned a vast, powerful system allowing the near real-time matching of citizens’ facial images risks a “profound chilling effect” on protest and dissent.

The technology – known in shorthand as “the capability” – collects and pools facial imagery from various state and federal government sources, including driver’s licences, passports and visas.

The biometric information can then rapidly – almost in real time – be compared with other sources, such as CCTV footage, to match identities.

The system, chiefly controlled by the federal Department of Home Affairs, is designed to give intelligence and security agencies a powerful tool to deter identity crime, and quickly identify terror and crime suspects.

But it has prompted serious concern among academics, human rights groups and privacy experts. The system sweeps up and processes citizens’ sensitive biometric information regardless of whether they have committed or are suspected of an offence.

Chinese ‘Gait Recognition’ Tech IDs People By How They Walk; Police Have Started Using It on Streets of Beijing and Shanghai

Already used by police on the streets of Beijing and Shanghai, “gait recognition” is part of a push across China to develop artificial-intelligence and data-driven surveillance that is raising concern about how far the technology will go. Huang Yongzhen, the CEO of Watrix, said that its system can identify people from up to 50 meters (165 feet) away, even with their back turned or face covered. This can fill a gap in facial recognition, which needs close-up, high-resolution images of a person’s face to work. “You don’t need people’s cooperation for us to be able to recognize their identity,” Huang said in an interview in his Beijing office. “Gait analysis can’t be fooled by simply limping, walking with splayed feet or hunching over, because we’re analyzing all the features of an entire body.”

Blockchain-based elections would be a disaster for democracy

If you talk to experts on election security they’ll tell you that we’re nowhere close to being ready for online voting. “Mobile voting is a horrific idea,” said election security expert Joe Hall when I asked him about a West Virginia experiment with blockchain-based mobile voting back in August.

But on Tuesday, The New York Times published an opinion piece claiming the opposite.

“Building a workable, scalable, and inclusive online voting system is now possible, thanks to blockchain technologies,” writes Alex Tapscott, whom the Times describes as co-founder of the Blockchain Research Institute.

Tapscott is wrong—and dangerously so. Online voting would be a huge threat to the integrity of our elections—and to public faith in election outcomes.

Tapscott focuses on the idea that blockchain technology would allow people to vote anonymously while still being able to verify that their vote was included in the final total. Even assuming this is mathematically possible—and I think it probably is—this idea ignores the many, many ways that foreign governments could compromise an online vote without breaking the core cryptographic algorithms.

For example, foreign governments could hack into the computer systems that governments use to generate and distribute cryptographic credentials to voters. They could bribe election officials to supply them with copies of voters’ credentials. They could hack into the PCs or smartphones voters use to cast their votes. They could send voters phishing emails to trick them into revealing their voting credentials—or simply trick them into thinking they’ve cast a vote when they haven’t.

Energy cost of ‘mining’ bitcoin more than twice that of copper or gold

The amount of energy required to “mine” one dollar’s worth of bitcoin is more than twice that required to mine the same value of copper, gold or platinum, according to a new paper, suggesting that the virtual work that underpins bitcoin, ethereum and similar projects is more similar to real mining than anyone intended.

One dollar’s worth of bitcoin takes about 17 megajoules of energy to mine, according to researchers from the Oak Ridge Institute in Cincinnati, Ohio, compared with four, five and seven megajoules for copper, gold and platinum.

Other cryptocurrencies also fair poorly in comparison, the researchers write in the journal Nature Sustainability, ascribing a cost-per-dollar of 7MJ for ethereum and 14MJ for the privacy focused cryptocurrency monero. But all the cryptocurrencies examined come off well compared with aluminium, which takes an astonishing 122MJ to mine one dollar’s worth of ore.

Facebook Allowed Advertisers to Target Users Interested in “White Genocide”—Even in Wake of Pittsburgh Massacre

Apparently fueled by anti-Semitism and the bogus narrative that outside forces are scheming to exterminate the white race, Robert Bowers murdered 11 Jewish congregants as they gathered inside their Pittsburgh synagogue, federal prosecutors allege. But despite long-running international efforts to debunk the idea of a “white genocide,” Facebook was still selling advertisers the ability to market to those with an interest in that myth just days after the bloodshed.

A simple search of Facebook pages also makes plain that there are tens of thousands of users with a very earnest interest in “white genocide,” shown through the long list of groups with names like “Stop White South African Genocide,” “White Genocide Watch,” and “The last days of the white man.” Images with captions like “Don’t Be A Race Traitor” and “STOP WHITE GENOCIDE IN SOUTH AFRICA” are freely shared in such groups, providing a natural target for anyone who might want to pay to promote deliberately divisive and incendiary hate-based content.

Only 22% of Americans Now Trust Facebook’s Handling of Personal Info

Facebook is the least trustworthy of all major tech companies when it comes to safeguarding user data, according to a new national poll conducted for Fortune, highlighting the major challenges the company faces following a series of recent privacy blunders. Only 22% of Americans said that they trust Facebook with their personal information, far less than Amazon (49%), Google (41%), Microsoft (40%), and Apple (39%).

In question after question, respondents ranked the company last in terms of leadership, ethics, trust, and image… Public mistrust extended to Zuckerberg, Facebook’s public face during its privacy crisis and who once said that Facebook has “a responsibility to protect your information, If we can’t, we don’t deserve it.” The company subsequently fell victim to a hack but continued operating as usual, including debuting a video-conferencing device intended to be used in people’s living rooms or kitchens and that further extends Facebook’s reach into more areas outside of personal computers and smartphones. Only 59% of respondents said they were “at least somewhat confident” in Zuckerberg’s leadership in the ethical use of data and privacy information, ranking him last among four other tech CEOS…

As for Facebook, the social networking giant may have a difficult time regaining public trust because of its repeated problems. Consumers are more likely to forgive a company if they believe a problem was an aberration rather than a systemic failure by its leadership, Harris Poll CEO John Gerzema said.

The article concludes that “For now, the public isn’t in a forgiving mood when it comes to Facebook and Zuckerberg.”

What Your Phone is Telling Wall Street

Your phone knows where you shop, where you work and where you sleep. Hedge funds are very interested in such data, so they are buying it.

When Tesla Chief Executive Elon Musk said the car maker would work around the clock to boost production of its Model 3 sedan, the number crunchers at Thasos Group decided to watch. They circled Tesla’s 370 acres in Fremont, Calif., on an online map, creating a digital corral to isolate smartphone location signals that emanated from within it. Thasos, which leases databases of trillions of geographic coordinates collected by smartphone apps, set its computers to find the pings created at Tesla’s factory, then shared the data with its hedge-fund clients [Editor’s note: the link may be paywalled; alternative source], showing the overnight shift swelled 30% from June to October.

Last month, many on Wall Street were surprised when Tesla disclosed a rare quarterly profit, the result of Model 3 production that had nearly doubled in three months. Shares shot up 9.1% the next day. Thasos is at the vanguard of companies trying to help traders get ahead of stock moves like that using so-called alternative data. Such suppliers might examine mine slag heaps from outer space, analyze credit-card spending data or sort through construction permits. Thasos’s specialty is spewing out of your smartphone.

Thasos gets data from about 1,000 apps, many of which need to know a phone’s location to be effective, like those providing weather forecasts, driving directions or the whereabouts of the nearest ATM. Smartphone users, wittingly or not, share their location when they use such apps. Before Thasos gets the data, suppliers scrub it of personally identifiable information, Mr. Skibiski said. It is just time-stamped strings of longitude and latitude. But with more than 100 million phones providing such coordinates, Thasos says it can paint detailed pictures of the ebb and flow of people, and thus their money.

When Tech Knows You Better Than You Know Yourself

Algorithms are kind of running where 2 billion people spend their time. Seventy percent of what people watch on YouTube is driven by recommendations from the algorithm. People think that what you’re watching on YouTube is a choice. People are sitting there, they sit there, they think, and then they choose. But that’s not true. Seventy percent of what people are watching is the recommended videos on the right hand side, which means 70 percent of 1.9 billion users, that’s more than the number of followers of Islam, about the number followers of Christianity, of what they’re looking at on YouTube for 60 minutes a day—that’s the average time people spend on YouTube. So you got 60 minutes, and 70 percent is populated by a computer. The machine is out of control.