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Dear @niwnews A great effort to explain the use of CCTV surveillance at Silent Valley. #privacy
7 hours ago by loughlin
Are Targeted Ads Stalking You? Here’s How to Make Them Stop - The New York Times
8/15/18 - ... Here are a few simple steps you can take if you are being pestered by an ad and want that to end:
Technology  Internet  Privacy  Ads 
12 hours ago by mcbakewl
Dear A great effort to explain the use of CCTV surveillance at Silent Valley. 👍
privacy  from twitter_favs
14 hours ago by loughlin
Google updates Location History language after tracking backlash – TechCrunch
The update was noted by the Associated Press, which first brought the tracking issue to light earlier this week in a report. Google initially denied its own inaccurate reporting, but later backtracked, adding that it had added clarifying language. v…
via-IFTTT  via-Pocket  google  maps  news  privacy  tech  via-Diigo 
15 hours ago by evansthompson
[1808.00023] The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning
The nascent field of fair machine learning aims to ensure that decisions guided by algorithms are equitable. Over the last several years, three formal definitions of fairness have gained prominence: (1) anti-classification, meaning that protected attributes---like race, gender, and their proxies---are not explicitly used to make decisions; (2) classification parity, meaning that common measures of predictive performance (e.g., false positive and false negative rates) are equal across groups defined by the protected attributes; and (3) calibration, meaning that conditional on risk estimates, outcomes are independent of protected attributes. Here we show that all three of these fairness definitions suffer from significant statistical limitations. Requiring anti-classification or classification parity can, perversely, harm the very groups they were designed to protect; and calibration, though generally desirable, provides little guarantee that decisions are equitable. In contrast to these formal fairness criteria, we argue that it is often preferable to treat similarly risky people similarly, based on the most statistically accurate estimates of risk that one can produce. Such a strategy, while not universally applicable, often aligns well with policy objectives; notably, this strategy will typically violate both anti-classification and classification parity. In practice, it requires significant effort to construct suitable risk estimates. One must carefully define and measure the targets of prediction to avoid retrenching biases in the data. But, importantly, one cannot generally address these difficulties by requiring that algorithms satisfy popular mathematical formalizations of fairness. By highlighting these challenges in the foundation of fair machine learning, we hope to help researchers and practitioners productively advance the area.
machine_learning  algorithms  bias  ethics  privacy  review  for_friends 
16 hours ago by rvenkat
AP Exclusive: Google tracks your movements, like it or not
Google wants to know where you go so badly that it records your movements even when you explicitly tell it not to.

An Associated Press investigation found that many Google services on Android devices and iPhones store your location data even if you’ve used a privacy setting that says it will prevent Google from doing so.

Computer-science researchers at Princeton confirmed these findings at the AP’s request.
2018-08  privacy  security  google 
17 hours ago by Weaverbird
The conviction of a computer scientist who searched “insider trading” should concern us all | The Outline
This isn’t me putting on a tinfoil hat, either. In 2013, the ACLU expressed concern that the Justice Department was improperly obtaining our search results, while last year, the organization pointed out that the vast majority of individuals whose internet activity is monitored by the government are never informed that they were surveilled in the first place. And within the government, Ron Wyden, the senior Democratic Senator from Oregon, has been dogged in his quest to understand the degree to which law enforcement has access to Americans’ data and how. Wyden recently called out the FBI for “misstat[ing] the number of devices rendered inaccessible by encryption” in what appeared to be an attempt to establish a legal right to access encrypted devices or to water down encryption technology so that they can gain access to more of our shit than they already have.
2018-08  privacy  surveillance  police_state 
17 hours ago by Weaverbird
Universal Viewer
The Universal Viewer is a community-developed open source project on a mission to help you share your content with the world
media  viewer  webapp  cool  image  video  opensource  sharing  privacy 
18 hours ago by orlin
Secure email: ProtonMail is free encrypted email.
Secure Email Based in Switzerland
Secure Your Communications with ProtonMail
email  hosting  privacy  security 
20 hours ago by eheiser

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