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Kromtech Security Researchers have discovered another publically accessible Amazon S3 repository. This time it contained medical data in 316,363 PDF reports in the form of weekly blood test results. Many of these were multiple reports on individual patients. It appears that each patient had weekly test results totaling around 20 files each. That would still be an estimated 150,000+ people affected by the leak.

Wearable sensor technology has the potential to transform health care and our understanding of our own bodies and habits. The investigation and testing of these sensors in the commercial sector offer an unprecedented opportunity to leverage biometric data, both to improve individual health through the development of better products and to advance the public good through research. However, research with wearable sensor data must be done in a manner that is respectful of ethical considerations and consumer privacy

Domino's pizza-company is about to test out a new feature in Australia, one that flips the script of allowing customers to track a pizza that's being delivered. Now the company wants track customers

23andMe announced what Forbes reports is only the first of ten deals with big biotech companies: Genentech will pay up to $60 million for access to 23andMe's data to study Parkinson's. You think 23andMe was about selling fun DNA spit tests for $99 a pop? Nope, it's been about selling your data all along.

A manifesto for Data Literacy in the scope of what the BBC should be doing

"While the Philips Hue system is based on open technologies we are not able to ensure all products from other brands are tested and fully interoperable with all of our software updates. For guaranteed compatibility you need to use Philips Hue or certified Friends of Hue products"

"Data Sense is a research experiment at Intel Labs. We wanted to see if it is possible to make data more accessible to those of us without stats degrees. To test out some ideas, we built this tool. "

London's Metropolitan Police have been urged to back down on plans to once again use facial recognition software at next weekend's Notting Hill Carnival. Privacy groups including Big Brother Watch, Liberty and Privacy International have written to police commissioner Cressida Dick (PDF) calling for a U-turn on the use of the tech. Automated facial recognition technology will snap the party-goers' faces, and run them against a database. The aim is to alert cops to people who are banned from the festival or are wanted by the police, presumably so they can take immediate action. The tech was first tested at the festival - where relations between police and revellers are often strained - last year, but it failed to identify anyone.

The blind application of machine learning runs the risk of amplifying biases present in data. Such a danger is facing us with word embedding, a popular framework to represent text data as vectors which has been used in many machine learning and natural language processing tasks. We show that even word embeddings trained on Google News articles exhibit female/male gender stereotypes to a disturbing extent. This raises concerns because their widespread use, as we describe, often tends to amplify these biases. Geometrically, gender bias is first shown to be captured by a direction in the word embedding. Second, gender neutral words are shown to be linearly separable from gender definition words in the word embedding. Using these properties, we provide a methodology for modifying an embedding to remove gender stereotypes, such as the association between between the words receptionist and female, while maintaining desired associations such as between the words queen and female. We define metrics to quantify both direct and indirect gender biases in embeddings, and develop algorithms to "debias" the embedding. Using crowd-worker evaluation as well as standard benchmarks, we empirically demonstrate that our algorithms significantly reduce gender bias in embeddings while preserving the its useful properties such as the ability to cluster related concepts and to solve analogy tasks. The resulting embeddings can be used in applications without amplifying gender bias.