UKG's Noemie Weinbaum and Roy Kamp explore differential privacy as a mathematically provable protection that can withstand the unpredictability of agentic AI systems.

In this work, we aim to develop a hardware-based technique to achieve differential privacy by design. In contrary to the conventional software-based noise generation and injection process, our design ...

*That's an interesting term of art. Lots better than saying "we stuck some noise in your Apple box to make it harder to spy on you." Gaining insight into the overall user population is crucial to ...

Joseph, Matthew, Jieming Mao, Seth Neel, and Aaron Leon Roth. "The Role of Interactivity in Local Differential Privacy." Proceedings of the IEEE Annual Symposium on ...

When the U.S. Census Bureau releases the first local results with demographic breakdowns from the 2020 decennial census on Thursday, the numbers will reflect a new method for protecting the details of ...

Hiding sensitive data in a sea of noise might have more value than encryption in some use cases. Here are the most likely differential privacy applications and their trade-offs. In the past, the ...

Apple is stepping up its artificial intelligence efforts in a bid to keep pace with rivals who have been driving full-throttle down a machine learning-powered AI superhighway, thanks to their liberal ...

Apple has added a new entry to its Machine Learning Journal with in-depth technical details about how it uses differential privacy to gather anonymous usage insights from devices like iPhones, iPads, ...