CSIS Seminar

Sensitive Data Analytics with Local Differential Privacy

Abstract

When collecting sensitive information, local differential privacy (LDP) can relieve users' privacy concerns, as it allows users to add noise to their private information before sending data to the server. LDP has been adopted by big companies such as Google and Apple for data collection and analytics. My research focuses on improving the ecosystem of LDP. In this talk, I will first share my research on the fundamental tools in LDP, namely the frequency oracles (FOs), which estimate the frequency of each private value held by users. We proposed a framework that unifies different FOs and optimizes them. Our optimized FOs improve the estimation accuracy of Google's and Apple's implementations by 50% and 90%, respectively, and serve as the state-of-the-art tools for handling more advanced tasks. In the second part of my talk, I will present our work on extending the functionality of LDP, namely, how to make a database system that satisfies LDP while still supporting a variety of analytical queries. Join Zoom Meeting https://gmu.zoom.us/j/97002679622?pwd=bmY0UHIrYjNEQTV5RWpRMkRaTWUyZz09 Meeting ID: 970 0267 9622 Passcode: 942948

Speaker Bio

Tianhao Wang is a Ph.D. candidate in the department of computer science, Purdue University, advised by Prof. Ninghui Li. He received his B.Eng. degree from software school, Fudan University in 2015. His research area is security and privacy, with a focus on differential privacy and applied cryptography. He is a member of DPSyn, which won several international differential privacy competitions. He is a recipient of the Bilsland Dissertation Fellowship and the Emil Stefanov Memorial Fellowship.