Jian Du, TikTok
Private Set Intersection (PSI) allows two parties, each holding an input set, to compute functions of corresponding values for shared set members, while maintaining the confidentiality of both the intersecting and non-intersecting elements. It has been used in applications such as ad providers and advertisers sharing privileged user behavior data to measure ad effectiveness, cloud storage operators detecting child exploitation material on users' encrypted cloud data, etc. A USENIX22' study found that common PSIs that disclose the intersection size can be vulnerable to attacks that exploit this disclosure, thereby revealing over 1% of one party's users to the other party in practical Ads measurement. To mitigate the risk of privacy leakage, we developed the DPCA-PSI protocol, which incorporates a novel PSI approach and a two-party differentially private (DP) mechanism, providing a secure means of computing intersection-related statistics from private datasets while preserving DP protection. DPCA-PSI is open-source and available to researchers and practitioners.
Authors: Jian Du, Haohao Qian, Bo Jiang, Yongjun Zhao, Shikun Zhang, and Qiang Yan
Jian Du, TikTok
Jian is a research scientist at TikTok, driving the research and development of privacy-enhancing technologies applied to TikTok's products.
At TikTok, Jian is leading the development of PrivacyGo, an open-source project available on GitHub (TikTok Privacy Innovation). PrivacyGo aims to synergistically fuse PETs to address real-world privacy challenges, such as combining secure multi-party computation and two-party differential privacy for enabling privacy-preserving ad measurement.
Prior to joining TikTok, Jian worked on PETs at Ant Financial and held a postdoctoral research position at Carnegie Mellon University.
author = {Jian Du},
title = {Protecting User Privacy in Private Set Intersection: A Journey Toward Mitigating User Tracking},
year = {2023},
address = {Santa Clara, CA},
publisher = {USENIX Association},
month = sep
}