Abstract : Many of us use smartphones and rely on tools like auto-complete and spelling auto-correct to make using these devices more pleasant, but building these tools presents a challenge. On the one hand, the machine-learning algorithms used to provide these features require data to learn from, but on the other hand, who among us is willing to send a carbon copy of all our text messages to device manufacturers to provide that data? 'Local differential privacy' and related concepts have emerged as the gold standard model in which to analyze tradeoffs between losses in utility and privacy for solutions to such problems. In this talk, we give a new state-of-the-art algorithm for estimating histograms of user data, making use of projective geometry over finite fields coupled with a reconstruction algorithm based on dynamic programming.
This talk is based on joint work with Vitaly Feldman (Apple), Huy Le Nguyen (Northeastern), and Kunal Talwar (Apple).

Keywords : differential privacy; local differential privacy; private frequency estimation