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Critical and insightful reading list about IOT

"Late yesterday European institutions finally agreed the text of new data protection rules (GDPR), more than three years after new regulation was proposed. The 28 Member States of the European Union will have two years to transpose the provisions of the GDPR into their national laws, with the regulation set to come into force from 2018."

"TalkTalk is facing increasing criticism after belatedly admitting it was the victim of a "significant and sustained" cyber-attack that has led to the theft of the credit card and bank details of up to 4 million customers. "

"A Ford safety feature has also turned out to be a way to track badly-behaved drivers."

The makers of the We-Vibe, a line of vibrators that can be paired with an app for remote-controlled use, have reached a $3.75 million class action settlement with users following allegations that the company was collecting data on when and how the sex toy was used. Standard Innovations, the Canadian manufacturer of the We-Vibe, does not admit any wrongdoing in the settlement finalized Monday.

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.

Say you're scrolling through your Facebook Newsfeed and you encounter an ad so eerily well-suited, it seems someone has possibly read your brain. Maybe your mother's birthday is coming up, and Facebook's showing ads for her local florist. Or maybe you just made a joke aloud about wanting a Jeep, and Instagram's promoting Chrysler dealerships. Whatever the subject, you've seen ads like this. You've wondered - maybe worried - how they found their way to you.

"The big companies developing them show no interest in fixing the problem."

Each Occupy.here router is a LAN island in an archipelago of affiliated websites. Build your own Darknet

Our system collects torrent files in two ways: parsing torrent sites and listening DHT network. We have more than 500.000 torrents which where classified and which are using now for collecting peer sharing facts (up to 700.000.000 daily). We don't guarantee we can show ALL peer sharing facts:

"You are being watched. Private and state-sponsored organizations are monitoring and recording your online activities. privacytools.io provides knowledge and tools to protect your privacy against global mass surveillance."

"Internet of Things bill of rights." core principles

Privacy icons for Mozilla...

Big data project for those on low incomes

Big data project for those on low incomes

"Only a couple of weeks ago, there were a lot of news headlines about how Germany had banned an internet-connected doll called "Cayla" over fears hackers could target children. One of their primary concerns was the potential risk to the privacy of children:"

"Leaked documents said to describe how the social network shares psychological insights on young people with advertisers"

"Facebook's collection of data makes it one of the most influential organisations in the world. Share Lab wanted to look "under the bonnet" at the tech giant's algorithms and connections to better understand the social structure and power relations within the company. "

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.