Extracting personal information from anonymous cell phone data using machine learning
A research team at Illinois Institute of Technology has extracted personal information, specifically protected characteristics like age and gender, from anonymous cell phone data using machine learning and artificial intelligence algorithms, raising questions about data security.
Lithium ion batteries are the go-to power source for many of our favorite devices like cell phones and laptops, and their presence will continue to expand as electric vehicles become the new standard, replacing gasoline-powered cars.
We revisit the problem of designing scalable protocols for private statistics and private federated learning when each device holds its private data. Locally differentially private algorithms require little trust but are (provably) limited in their utility. Centrally differentially private algorithms can allow significantly better utility but require a trusted curator.…