Chinese-Made Smartphones Are Secretly Stealing Money From People Around The World

Preinstalled malware on low-cost Chinese phones has stolen data and money from some of the world’s poorest people. Read More

#5g, #china, #privacy

Identity Recognition Based on Bioacoustics of Human Body

Current biometrics rely on images obtained from the structural information of physiological characteristics, which is inherently a fatal problem of being vulnerable to spoofing. Here,we studied personal identification using the frequency-domain information based on human body vibration. We developed a bioacoustic frequency spectroscopy system and applied it to the fingers to obtain information on the anatomy, biomechanics, and biomaterial properties of the tissues. As a result, modulated microvibrations propagated through our body could capture a unique spectral trait of a person and the biomechanical transfer characteristics persisted for two months and resulted in 97.16%accuracy of identity authentication in 41 subjects. Ultimately, our method not only eliminates the practical means of creating fake copies of the relevant characteristics but also provides reliable features. Read More

#cyber, #privacy

Researchers Have Found a Way to Listen In On Your Conversations Using Light Bulb Vibrations

As if we didn’t have enough to be stressed about in 2020, researchers from the Cyber Security Labs at Ben Gurion University and the Weizmann Institute of Science have come up with a way to listen in on a room, even at long distances, using less than $1,000 worth of equipment that’s able to measure subtle light changes in a room caused by sound waves vibrating a light bulb. Read More

#cyber, #nlp, #privacy

Enigma: Decentralized Computation Platform with Guaranteed Privacy

A peer-to-peer network, enabling different parties to jointly store and run computations on data while keeping the data completely private. Enigma’s computational model is based on a highly optimized version of secure multi-party computation,guaranteed by a verifiable secret-sharing scheme. For storage, we use a modified distributed hash table for holding secret-shared data. An external blockchain is utilized as the controller of the network, manages access control, identities and serves as a tamper-proof log of events. Security deposits and fees incentivize operation, correctness and fairness of the system. Similar to Bitcoin, Enigma removes the need for a trusted third party, enabling autonomous control of personal data.For the first time, users are able to share their data with cryptographic guarantees regarding their privacy. Read More

#privacy

Amazon explores a way to preserve privacy in natural language processing

Can privacy and security be preserved in the course of large-scale textual data analysis? As it turns out, yes. A team of Amazon researchers in a recently published study proposed a way to anonymize customer-supplied data. They claim that their approach, which works by rephrasing samples and basing the analysis on the new phrasing, results in at least 20-fold greater guarantees on expected privacy. Read More

#privacy

The Secretive Company That Might End Privacy as We Know It

The New York Times has a long story about a little-known start-up, Clearview AI, that helps law enforcement match photos of unknown people to their online images — and “might lead to a dystopian future or something,” a backer says. Read More

#image-recognition, #privacy

How federated learning could shape the future of AI in a privacy-obsessed world

You may not have noticed, but two of the world’s most popular machine learning frameworks — TensorFlow and PyTorch — have taken steps in recent months toward privacy with solutions that incorporate federated learning.

Instead of gathering data in the cloud from users to train data sets, federated learning trains AI models on mobile devices in large batches, then transfers those learnings back to a global model without the need for data to leave the device. Read More

#federated-learning, #privacy

Twelve Million Phones, One Dataset, Zero Privacy

Every minute of every day, everywhere on the planet, dozens of companies — largely unregulated, little scrutinized — are logging the movements of tens of millions of people with mobile phones and storing the information in gigantic data files. The Times Privacy Project obtained one such file, by far the largest and most sensitive ever to be reviewed by journalists. It holds more than 50 billion location pings from the phones of more than 12 million Americans as they moved through several major cities, including Washington, New York, San Francisco and Los Angeles.

Each piece of information in this file represents the precise location of a single smartphone over a period of several months in 2016 and 2017. Read More

#cyber, #privacy, #surveillance, #wifi

Building a World Where Data Privacy Exists Online

Data is valuable — something that companies like Facebook, Google and Amazon realized far earlier than most consumers did. But computer scientists have been working on alternative models, even as the public has grown weary of having their data used and abused.

Dawn Song, a professor at the University of California, Berkeley, and one of the world’s foremost experts in computer security and trustworthy artificial intelligence, envisions a new paradigm in which people control their data and are compensated for its use by corporations. Read More

#adversarial, #assurance, #privacy

Google is open-sourcing a tool for data scientists to help protect private information

Google today announced that it is open-sourcing its so-called differential privacy library, an internal tool the company uses to securely draw insights from datasets that contain the private and sensitive personal information of its users.

Differential privacy is a cryptographic approach to data science, particularly with regard to analysis, that allows someone relying on software-aided analysis to draw insights from massive datasets while protecting user privacy. It does so by mixing novel user data with artificial “white noise,” as explained by Wired’s Andy Greenberg. That way, the results of any analysis cannot be used to unmask individuals or allow a malicious third party to trace any one data point back to an identifiable source. Read More

#homomorphic-encryption, #privacy