An NSA attack on China has blown up in America’s face.
Category: privacy
Keeping track of all Facebook’s scandals could easily be seen as a part-time job right now. All joking aside, the development of its worldwide network ramifications participated in decreasing communication distances between individuals with three and a half degrees of separation in average between its members in 2016.
As a reminder, this network and its numerous variations, which certainly don’t need to be quoted anymore, have enabled us
— to reach our friends, family members, business collaborators or partners
— to create and join groups of discussion
— to organize events
— to promote icons and push contents of very different shapes
The actual downside here is that all of it became possible from the very moment that we accepted to join the online club for free. The benefits of being able to access brand new and pretty efficient communication means, have left us with no choice but to keep returning again and again to a highly segmented network (made from both acquaintances and closed ones). Such network, of which more and more of our “friends” form part, manage to convince us to never really read the terms and conditions of use in their full details (who says boring?) even though they clearly involve nonetheless the real-time sale of a stream of our personal profiles and somewhat predictable behaviors as soon as we consciously tick the right (wrong?) boxes in order to just get in at some point.
Taking benefits in using a free service could be very different from being the actual provider of a stream of values (somewhat made available from analytics data and advertising market places) by acting in good faith as we would simply do in real life, by not accepting advertisement as the only existing way how to endorse some of our cultural preferences with regards to this or that innovative trends (when there is little innovation and not only unsustainable waste of our limited resources — time or namely our attention to mention some of them).
Today, the privacy advocates can also be thrilled by the broad variety of initiatives enabling us to restrain ourselves from dissipating our shared moments between our interlocutors AND third parties interfering with our conversations. How to progressively upgrade the software without requiring everybody (who feels like it could be a good idea, of course) to get on board? I guess we’re facing quite a pickle here, perhaps not as hard to take on as gluing back together large blocks of melting ice, but still not that trivial when considered at scale.
Here are two technological means how to get connected with your peers outside of what might have become the most “normative ways” and another one relying upon one of the oldest network there is –emails-, and their respective slogans:
— Element — Own your conversations — https://element.io
its underlying protocols are nowadays relied upon by the french state for some of its administration services
— Manyverse — A social network off the grid — https://www.manyver.se
— Delta Chat — Chat over email with encryption, like Telegram or messaging apps owned by Facebook but without the tracking or central control — https://delta.chat/en/
I would invite you to try them out, to share your insight about them and to support them and their contributors. Social challenges won’t only be taken care of by switching communication tools but conversations remain conversations and the better the host, the more comfortable and safe some could feel in preserving a discussion as open, honest and respectful as possible.
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Facial recognition is going mainstream. The technology is increasingly used by law-enforcement agencies and in schools, casinos and retail stores, spurring privacy concerns. In this episode of Moving Upstream, WSJ’s Jason Bellini tests out the technology at an elementary school in Seattle and visits a company that claims its algorithm can identify potential terrorists by their facial features alone.
Are we about to see Apple reaching an even newer gold standard in ear buds? Apple has a patent for AirPods with built in biometrics. It’s being called “universal” because the idea calls for ear pods that can be worn in either ear. That would mean no more left-right business.
Much of the tech news coverage is focusing on the “interchangeable” aspect of the patent concept. 9to5Mac’s Alex Allegro said interchangeable earbuds “equipped with ‘at least one’ biometric sensor, which could detect left/right ear placement and accordingly adjust audio.” He assessed the potential advantages as “evolving AirPods from a separate left / right unit to a single component,” which could lower costs and simplify getting a replacement AirPod.
If the ear buds are interchangeable, then it is a big deal in terms of user satisfaction. OK, the product is popular now but The Verge reminds us that the AirPod is not a perfect fit for everyone. Nick Statt: “Due to the shape of some people’s inner ear, the headphones simply don’t fit every possible ear shape well.”
The new service lets consumers contribute to medical research, but still poses privacy concerns.
- By Karen Weintraub on November 17, 2018
Biometric features like fingerprint sensors and iris scanners have made it easier to securely unlock phones, but they may never be as secure as a good old-fashioned password. Researchers have repeatedly worked out methods to impersonate registered users of biometric devices, but now a team from New York University and the University of Michigan has gone further. The team managed to create so-called “DeepMasterPrints” that can fool a sensor without a sample of the real user’s fingerprints.
Past attempts to bypass biometric systems usually involve getting access to a registered individual’s data — that could be a copy of their fingerprint or a 3D scan of their face. DeepMasterPrints involves generating an entirely new fingerprint from a mountain of data that’s close enough to fool the sensor. Like so many research projects these days, the team used neural networks to do the heavy lifting.
The process started with feeding fingerprints from 6,000 people into a neural network in order to train it on what a human fingerprint looks like. A neural network is composed of a series of nodes that process data. It feeds forward into additional “layers” of nodes if the output meets a certain threshold. Thus, you can train the network to get the desired output. In this case, the researchers used a “generative adversarial network” to tune the system’s ability to generate believable fingerprints. The network used its understanding of prints to make one from scratch, and then a second network would determine if they were real or fake. If the fingerprints didn’t pass muster, the network could be re-tuned to try again.