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" (LDP) and related models have become the gold standard for understanding the tradeoffs possible between utility and privacy loss. In this talk we present a new LDP mechanism for estimating data histograms over large numbers of users, making use of projective geometry together with a dynamic programming based reconstruction algorithm. I will also mention the opportunity for tools from this community to have impact in mobile devices, e.g. the SQKR mechanism of [Chen, Kairouz, Ozgur'20] on private mean estimation using work on Kashin representations by Lyubarskii and Vershynin. This talk is based on joint work with Vitaly Feldman (Apple), Huy Le Nguyen (Northeastern), and Kunal Talwar (Apple).