And you are here why?

Anonymization Image recognition is one of the areas that Deep Learning excels at. It is, in fact, so good at it that these days you can pretty easily identify anyone from a photograph (especially if you’ve got a large enough corpus of identity information, like Facebook, Google, or, oh, the Feds). So, how d’you go about anonymizing pictures? Let’s make this legit — you’re the newspaper (look it up, it’s a thing), and you want to publish a picture of a witness in your ScoopOfTheYear™ along with the story. You’d probably do something like this picture, right? An image with a nicely blurred face, can’t make out who it is, etc. etc. Blurring: Not the Answer Well, you guessed wrong! Back in 2016,   McPherson et al.   (•) showed that you can uniquely identify people from these types of blurred images. (You’ve got to remember, Neural Networks don’t give a s**t about whether a thing is a “face” or not, it just matches away…). What’s even worse is that Shiri et al. (••) figured o...

Style Transfer using #DeepLearning  has been getting easier — it’s gone from “minutes per image” to “almost real time” in less than a year, and will probably be part of your camera app Any Day Now. The reverse however, where you convert the stylized image back to the original, is something that not too many people are looking at (Anyone? Bueller? Bueller?). Enter a new paper by Shiri et al., where they use an FDNN ( Face Destylization Neural Network ) to restore a photo-realistic face from a stylized one, as shown below What the FDNN does is, basically a) Identify specific facial components from the stylized images b) Map these components to components in real/actual faces c) Mush the result into a “real” face by smoothing out transitions, fitting, fixing, etc. (e.g. “ No, the cheeks can’t be 70% of the face. Not even BadPlasticSurgery will do that ”) When it all comes together, it works remarkably well , but there is still a ways to go. For example, it currentl...

Apple released a little more information about #DifferentialPrivacy with a paper and an excellent blog post (°). A quick refresher - Under differential privacy, you • Collect user’s data (locally, or on the server) • Add noise to mask the data, but in such a way that when you • Average out the data, the noise averages out, and meaningful information emerges The trick, of course, is in the details, at the very least, you need to be careful in • calibrating the noise so that it averages out (algorithms matter!) • collecting and transmitting information in a manner that protects privacy (secure storage! encryption!) • keeping the information under privacy exposure thresholds (don’t combine data across different use cases! restrict the number of use cases!) etc. It's a fascinating read, and, I suspect, the very beginning of the field. I expect signal processing (Huffman coding, etc) to come into play any day now   (°) Learning with Privacy at Scale - the paper - h...

Is anybody surprised? Even slighly? If so, why ? I mean, this is Facebook we are talking about - where " Privacy " is usually translated to " Huh? Whats That? " /via Dork Tower BTW, a good rebuttal to apologists here

There is a pretty huge meme going around right now about how "Google Drive is stealing your data".  Typical examples on twitter include @pdparticle - WOW! Google pretty much owns your data once uploaded to Google Drive. t.co/YUIqIVtM #GoogleDrive Google Drive owns everything you upload: terms of service analysis t.co/MDuZZ52R The truth, as in most of these cases, is a lot more complicated.  In a nutshell, if you want to blame anybody/anything, blame the copyright laws . First of all, just to get this out of the way, Google explicitly says   Some of our Services allow you to submit content. You retain ownership of any intellectual property rights that you hold in that content. In short, what belongs to you stays yours Moving on, consider what rights Google needs to just do what it does on a day to day basis In fact, do this backwards - if you store your data on a hosted service, anywhere , then you have to assign a whole bushel of rights to that service fo...