Posts Tagged ‘privacy’

Trans-Atlantic data sharing rules under GDPR

February 14, 2020

https://www.ga4gh.org/news/ga4gh-gdpr-brief-standard-contractual-clauses-opinion-of-the-advocate-general-in-schrems-ii/

nothing to hide, something to lose

February 9, 2020

https://utpjournals.press/doi/10.3138/utlj.2018-0118

Your Phone Is Listening and it’s Not Paranoia

January 27, 2020

QT:{{”

““From time to time, snippets of audio do go back to [other apps like Facebook’s] servers but there’s no official understanding what the triggers for that are,” explains Peter. “Whether it’s timing or location-based or usage of certain functions, [apps] are certainly pulling those microphone permissions and using those periodically. All the internals of the applications send this data in encrypted form, so it’s very difficult to define the exact trigger.””
“}}

Your Phone Is Listening and it’s Not Paranoia
https://www.vice.com/en_uk/article/wjbzzy/your-phone-is-listening-and-its-not-paranoia

In Germany, controversial law gives Bavarian police new power to use DNA | Science | AAAS

January 27, 2020

http://www.sciencemag.org/news/2018/05/germany-controversial-law-gives-bavarian-police-new-power-use-dna

How Smart TVs in Millions of U.S. Homes Track More Than What’s On Tonight

January 27, 2020

https://www.nytimes.com/2018/07/05/business/media/tv-viewer-tracking.html

Kinship structure inference

January 25, 2020

Ancient genome-wide analyses infer kinship structure in an Early Medieval Alemannic graveyard
http://advances.sciencemag.org/content/4/9/eaao1262

Opinion | You Are Now Remotely Controlled – The New York Times

January 25, 2020

https://www.nytimes.com/2020/01/24/opinion/sunday/surveillance-capitalism.html

Colleges are turning students’ phones into surveillance machines – The Washington Post

January 24, 2020

A rather worrisome article, with a great quote: “Building technology was a lot more fun before it went all 1984.”

QT:{{”
But the perils of increasingly intimate supervision — and the subtle way it can mold how people act — have also led some to worry whether anyone will truly know when all this surveillance has gone too far. “Graduates will be well prepared … to embrace 24/7 government tracking and social credit systems,” one commenter on the Slashdot message board said. “Building technology was a lot more fun before it went all 1984.”
“}}

https://www.washingtonpost.com/technology/2019/12/24/colleges-are-turning-students-phones-into-surveillance-machines-tracking-locations-hundreds-thousands/

Fooling Big Brother – As face-recognition technology spreads, so do ideas for subverting it | Science and technology | The Economist

January 23, 2020

QT:{{”

In 2010, for instance, as part of a thesis for a master’s degree at New York University, an American researcher and artist named Adam Harvey created “cv [computer vision] Dazzle”, a style of make-up designed to fool face recognisers. It uses bright colours, high contrast, graded shading and asymmetric stylings to confound an algorithm’s assumptions about what a face looks like. To a human being, the result is still clearly a face. But a computer—or, at least, the specic algorithm Mr Harvey was aiming at—is ba ed. …
An even subtler idea was proposed by researchers at the Chinese University of Hong Kong, Indiana University Bloomington, and Alibaba, a big Chinese information-technology rm, in a paper published in 2018. It is a baseball cap tted with tiny light-emitting diodes that project infra-red dots onto the wearer’s face. Many of the cameras used in face-recognition systems are sensitive to parts of the infra-red spectrum. Since human eyes are not, infra-red light is ideal for covert trickery.
“}}

https://www.economist.com/science-and-technology/2019/08/15/as-face-recognition-technology-spreads-so-do-ideas-for-subverting-it

Opinion | How to Track President Trump – The New York Times

January 16, 2020

https://www.nytimes.com/interactive/2019/12/20/opinion/location-data-national-security.html QT:{{”
“With no training and far more limited technical tools than those of a state intelligence service, we were able to use the location data — date, time and length of stay — to make basic inferences. By determining whether two people were in the same place at the same time, it was easy to zero in on spouses, co-workers or friends. Cataloguing their movements revealed even more associations, creating the map of a robust social network that would be nearly impossible to determine through traditional surveillance. In cases where it was difficult to identify an individual, associations offered more clues about workplaces and interests.”
“}}