NSF CAREER: Exact Optimal and Data-Adaptive Algorithms and Tools for Differential Privacy

Principal Investigator
Yu-Xiang Wang, University of California at Santa Barbara
Project Summary

Funded by NSF CNS 2048091.

This material is based upon work supported by the National Science Foundation under Grant No. 2048091. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Project Summary

This project is motivated by the increasing public concerns on privacy issues, new legislations and the high demand for privacy enhancing technologies such as differential privacy (DP) in applications from both private and public sectors. The overarching theme of the project is to address the pressing new challenges that arise as differential privacy transforms from a theoretical construct into a practical technology. The project advances the state-of-the-art of research in the area of DP, and contributes to privacy education. On the research front, the project develops new algorithms and analytical tools that enable more precise privacy accounting and higher utility in DP. On the education front, the project involves training future leaders in DP areas, creating educational materials and expanding an open-source software library called autodp that makes state-of-the-art differentially private computation more accessible. Collectively, the integrated research and educational activities contribute to ongoing collaborative efforts in building innovative applications of differential privacy.

Fig 1. The exact optimal DP accouinting involves describing a DP mechanism by a function.

Talks and media coverages

  1. Invited talk on "Optimal Accounting of Differential Privacy via Characteristic Function"
    PI Wang spoke at Rutgers. Jinshuo and Yuqing spoke at Google.
  2. Invited talk on "Per-instance Differential Privacy and How to Privately Publish Them"
    PI Wang spoke at Google and at University of Albany
  3. Invited talk on "Advances in Differential Privacy and Private Federated Learning"
    PI Wang spoke at Amazon Web Services.
  4. News article "A Matter of Privacy"
    Coverage on this project on UCSB Magazine "The Current" [Link]
  5. Invited talk on "Privacy Amplification by Sampling and Renyi Differential Privacy"
    PI Wang spoke at Berkeley Simons Institute [Link to the talk]

Research Results

  1. Adaptive Private-K-Selection with Adaptive K and Application to Multi-label PATE
    Yuqing Zhu, Yu-Xiang Wang.
    AISTATS 2022 (to appear). [preprint available soon]
  2. Optimal Accounting of Differential Privacy via Characteristic Function
    Yuqing Zhu, Jinshuo Dong, Yu-Xiang Wang.
    AISTATS 2022. [arxiv]
  3. Privately Publishable Per-instance Privacy
    Rachel Redberg, Yu-Xiang Wang.
    NeurIPS 2021. [arxiv]
  4. Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning
    Chong Liu, Yuqing Zhu, Kamalika Chaudhuri, Yu-Xiang Wang
    Journal of Machine Learning Research. Shorter version appeared in AISTATS 2021. [arxiv]
  5. Voting-based Approaches For Differentially Private Federated Learning
    Yuqing Zhu, Xiang Yu, Yi-Hsuan Tsai, Francesco Pittaluga, Masoud Faraki, Manmohan chandraker and Yu-Xiang Wang
    Manuscript. [arxiv]
  6. Subsampled Renyi Differential Privacy and Analytical Moments Accountant
    Yu-Xiang Wang, Borja Balle, Shiva Kasiviswanathan
    Journal of Privacy and Confidentiality, 2021 [ paper ]. A shorter version appeared at AISTATS 2019 and received a Notable Paper Award.

Educational materials

  1. CS291A Introduction to Differential Privacy: Theory, Algorithms and Applications [Course website]
    Instructor: Yu-Xiang Wang, 2021 Fall
  2. [Open source project] New API / examples / tutorials / unit tests for autodp
    Contributors: Yu-Xiang Wang, Yuqing Zhu, Borja Balle (DeepMind), Stefan Mallem (Google)

Fig 2. An illustration of the new API of autodp.