PAL: Privacy and Anonymity Lab

Our Mission:  We strive to become a cutting-edge research group dedicated to advancing and understanding how privacy and anonymity contribute to the fundamental aspect of liberty in digital spaces, particularly in computer networks. PAL’s mission includes studying and developing technologies and methodologies to enhance user privacy, protect anonymity, and ensure that these elements support the broader concept of liberty in an increasingly connected world.

Our goal: We typically publish in top security and privacy conferences (Security, NDSS, CCS, S&P, EuroS&P, ACSAC, PETS), and collaborate with research institutions across the world (UC Irvine, University of Virginia, CISPA). 

Who we are looking for: Candidates should have experience with Python and a fundamental understanding of statistics and computer science/engineering. If you are interested, please email your resume to jchen27@sdsu.edu

Preferred Qualifications:

  • Proficiency with at least one of the following technologies:
    (i) Differential Privacy, (ii) Privacy Enhancing Technologies (for Database, Network Systems & Applications, …) (iii) Privacy Attacks and Auditing for ML model risks
  • Proficiency in Python
  • Ability to formulate problems, design and implement analytical and/or algorithmic solutions
  • Strong analytical skills and the ability to quickly learn, apply, and share new knowledge
  • Ability to collaborate and communicate effectively within a team environment

Publications

A Comprehensive Study of Privacy Risks in Curriculum Learning. Accepted by the 25th Privacy Enhancing Technologies Symposium (PETS), July 2025.
Joann Qiongna Chen, Xinlei He, Zheng Li, Yang Zhang, Zhou Li. 

NetDPSyn: Synthesizing Network Traces under Differential Privacy. Accepted by the 24th ACM Internet Measurement Conference (IMC), November 2024.
Danyu Sun, Joann Qiongna Chen, Chen Gong, Tianhao Wang and Zhou Li.

Differentially Private Resource Allocator. Accepted by the 39th Annual Computer Security Applications Conference (ACSAC), December 2023. & Theory and Practice of Differential Privacy (TPDP) Workshop, 2023.
Joann Qiongna Chen, Tianhao Wang, Zhikun Zhang, Yang Zhang, Somesh Jha, Zhou Li. 

Hide and Seek: Revisiting DNS-based User Tracking. Accepted by 7th IEEE European Symposium on Security and Privacy (EuroS&P), June 2022.
Deliang Chang*, Joann Qiongna Chen*, Zhou Li, Xing Li.  

Continuous Release of Data Streams under both Centralized and Local Differential Privacy. Accepted by the 28th ACM Conference on Computer and Communications Security (CCS), Virtual, November 2021.
Tianhao Wang, Joann Qiongna Chen, Zhikun Zhang, Dong Su, Yueqiang Cheng, Zhou Li, Ninghui Li, Somesh Jha. 

From WHOIS to WHOWAS: A Large-Scale Measurement Study of Domain Registration Privacy under the GDPR. In Proceedings of the 28th Annual Network and Distributed System Security Symposium (NDSS), Virtual, February 2021.
Baojun Liu, Chaoyi Lu, Yiming Zhang, Zhou Li, Fenglu Zhang, Haixin Duan, Ying Liu, Joann Qiongna Chen, Jinjin Liang, Zaifeng Zhang, Shuang Hao, Min Yang. 

Splitting Algorithm of Valuation Algebra and Its Application in Automaton Representation of Semiring Valued Constraint Problems. Quantitative Logic and Soft Computing 2016 Springer, Cham, 2017. 203-212.
Bang-He Han, Yong-Ming Li, Joann Qiongna Chen. 

*First two authors contributed equally to this work.