Demonstrating a potential privacy breach, a team of Princeton University engineers has developed an app that can locate and track people through their smartphones even when access to the Global Positioning System, or GPS, data on their devices is turned off.
A post saying “good morning” in Arabic was reportedly mistranslated to say “attack them” in Hebrew.
Israeli police arrested a Palestinian man last week after a Facebook post he made saying “good morning” in Arabic was mistranslated to read “attack them” in Hebrew, local media have reported.
Police confirmed that the construction worker was briefly held under suspicion of incitement but was released as soon as the mistake was realised.
The post showed a photo of the worker next to a bulldozer in the West Bank.
Such vehicles have been used to attack Israelis in the past.
There is only one difference in lettering between the colloquial Arabic phrase for “good morning to you all” and “hurt them”, pointed out The Times of Israel.
Privacy concerns have long swirled around how much information online advertising networks collect about people’s browsing, buying and social media habits — typically to sell you something.
But could someone use mobile advertising to learn where you go for coffee? Could a burglar establish a sham company and send ads to your phone to learn when you leave the house? Could a suspicious employer see if you’re using shopping apps on work time?
The answer is yes, at least in theory. New University of Washington research, to be presented in a paper Oct. 30 at the Association for Computing Machinery’s Workshop on Privacy in the Electronic Society, suggests that for roughly $1,000, someone with devious intent can purchase and target online advertising in ways that allow them to track the location of other individuals and learn what apps they are using.
PrivacyStreams Helps Developers Create Privacy Friendly Apps
Decision To Share Personal Data Need Not Be All or Nothing
A smartphone app that uses the raw feed from a device’s microphone or accesses its contact list can raise red flags for a user concerned about privacy. In many cases, however, the app doesn’t need all the details that users find most sensitive.
Researchers at Carnegie Mellon and Peking universities have addressed this dilemma by creating a service, PrivacyStreams, that enables app developers to access the smartphone data they need for app functionality while assuring users that their private information isn’t being sold to an online marketer or otherwise revealed.
A sleep-monitoring app, for instance, might need to access the smartphone’s microphone, but only to register loudness, not to monitor conversations. An app developer could simply sample the microphone feed every minute or so, use software in the PrivacyStreams library to transform the raw data to loudness and then send just the loudness data back to the smartphone for use by the app.
The EU-funded AMBER (Enhanced Mobile Biometrics) project has done just this. The project has shown it can identify gender by breaking down gestures and analysing the way in which users swipe screens using multiple datasets. In a recently published paper the team detail the software and protocol used for data collection, the feature set extracted and subsequent machine learning analysis. The results of this exploratory analysis have confirmed the possibility of sex prediction from the swipe gesture data, obtaining an encouraging 78% accuracy rate using swipe gesture data from two different directions.