Security/Learning Linkdump

I’ve accumulated a big collection of links this summer that are roughly related to security and/or machine learning and mostly connected to personal identification or human characteristics that I’m intending to share with my senior students when they return to campus in a few weeks. Having just noticed quite how large the collection has grown, it seems kind to pull them together into a semi-organized structure, as compared to my original plan of hitting send on an email filled with URLs, for their sake as well as my own. Taken together, it’s a nice little reading list.

How your smartphone’s battery life can be used to invade your privacy: “A little-known feature of the HTML5 specification means that websites can find out how much battery power a visitor has left on their laptop or smartphone – and now, security researchers have warned that that information can be used to track browsers online.” (see also the original research paper The leaking battery: A privacy analysis of the HTML5 Battery Status API)

Gone (Cat)Fishing: How Language Dectives Tackle Online Anonymity: “According to Dr Tim Grant in an article for The Conversation, “everything from the way someone uses capitalization or personal pronouns, to the words someone typically omits or includes, to a breakdown of average word or sentence length, can help identify the writer of even a short text like a Tweet or text message.” So it might surprise you how much of your individual writing style you leave behind for linguists to rifle through, even if you are a success at pretending to be someone different on the internet.” This article as a whole is a treasure trove of links surveying forensic linguistics.

Typing patterns are the latest anonymity-shattering personal identifier: Several links here discussing behavioral biometrics, its various uses, and a bit of coverage of how to avoid it (most specifically to this case, the Keyboard Privacy Chrome extension).

Face Recognition by Thermal Imaging: “The technology identifies a person from their thermal signature and matches infrared images with ordinary photos. It uses a deep neural network system to process the pictures and recognise people in bad light or darkness.” (from the first linked article)

Facial recognition technology is everywhere. It may not be legal.: “There are no federal laws that specifically govern the use of facial recognition technology. But while few people know it, and even fewer are talking about it, both Illinois and Texas have laws against using such technology to identify people without their informed consent. That means that one out of every eight Americans currently has a legal right to biometric privacy.”

Yet Another New Biometric: Brainprints: “In “Brainprint,” a newly published study in academic journal Neurocomputing, researchers from Binghamton University observed the brain signals of 45 volunteers as they read a list of 75 acronyms, such as FBI and DVD. They recorded the brain’s reaction to each group of letters, focusing on the part of the brain associated with reading and recognizing words, and found that participants’ brains reacted differently to each acronym, enough that a computer system was able to identify each volunteer with 94 percent accuracy. The results suggest that brainwaves could be used by security systems to verify a person’s identity.”

Personal microbiomes shown to contain unique ‘fingerprints’: “A new study shows that the microbial communities we carry in and on our bodies—known as the human microbiome—have the potential to uniquely identify individuals, much like a fingerprint. Harvard T.H. Chan School of Public Health researchers and colleagues demonstrated that personal microbiomes contain enough distinguishing features to identify an individual over time from among a research study population of hundreds of people.” An interesting possible implication is having to revisit the anonymity assumptions for biological samples.

Privacy Badger 1.0 Is Here To Stop Online Tracking!: “The new Privacy Badger 1.0 release includes many improvements, including being able to detect certain kinds of super-cookies and browser fingerprinting—some of the more subtle and problematic methods that the online tracking industry employs to follow Internet users from site to site.”

New research suggests that hackers can track subway riders through their phones: “Every subway in the world has a unique fingerprint, the researchers said, and every time a train runs between two stations, that fingerprint can be read in the accelerometer, potentially giving attackers access to crucial information. […] To make this attack a reality, the researchers propose a new attack that learns each subway’s fingerprint and then installs malware on a target’s phone that steals accelerometer readings.”

Computer learning system detects emotional context in text messages: “The quantification was carried out by examining 5,000 posts on social media pages and, through statistical analysis, gearing a learning system to recognize content structure that could be identified as condescending or slang. The system was constructed to identify key words and grammatical habits that were characteristic of sentence structure implied by the content’s sentiments.”

Google Research Boosts Pedestrian Detection with GPUs: “Outside of providing web-based services (for instance, automatically tagging images or picking out semantic understanding from video) the real use cases for how GPUs will power real-time services off the web are still developing. Pedestrian detection is one of those areas where, when powered by truly accurate and real-time capabilities, could mean an entirely new wave of potential services around surveillance, traffic systems, driverless cars, and beyond.” The Google focus is on solving the problem not just in real-time but with high accuracy.

Can a New Smartphone App Predict GPA?: “We show that there are a number of important behavioral factors automatically inferred from smartphones that significantly correlate with term and cumulative GPA, including time series analysis of activity, conversational interaction, mobility, class attendance, studying, and partying. We propose a simple model based on linear regression with lasso regularization that can accurately predict cumulative GPA.” (from research paper abstract)

Analysing galaxy images with artificial intelligence: astronomers teach a machine how to ‘see’: “Mr Hocking, who led the new work, commented: “The important thing about our algorithm is that we have not told the machine what to look for in the images, but instead taught it how to ‘see’.” His supervisor and fellow team member Dr James Geach added: “A human looking at these images can intuitively pick out and instinctively classify different types of object without being given any additional information. We have taught a machine to do the same thing.””

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