Research Areas

• Security
• Artificial Intelligence
• Algorithms & Theory

Research Interests

• Data and Machine Learning Privacy
• Trustworthy Machine Learning

Profile

Reza Shokri is a NUS Presidential Young Professor of Computer Science. His research focuses on trustworthy machine learning, quantitative analysis of data privacy, and design of privacy-preserving algorithms for practical applications, ranging from data synthesis to collaborative machine learning. He is an active member of the security and privacy community, and has served as a PC member of IEEE S&P, ACM CCS, Usenix Security, NDSS, and PETS. He received the Caspar Bowden Award for Outstanding Research in Privacy Enhancing Technologies in 2018, for his work on analyzing the privacy risks of machine learning models, and was a runner-up in 2012, for his work on quantifying location privacy. He obtained his PhD from EPFL.

Current Projects

• Privacy in machine learning
• Privacy-preserving data synthesis
• Robust collaborative machine learning
• Interpretable yet privacy-preserving machine learning

Selected Publications

• Liwei Song, Reza Shokri, and Prateek Mittal 
  Privacy Risks of Securing Machine Learning Models against Adversarial Examples 
  In ACM Conference on Computer and Communications Security (CCS), 2019.

• Milad Nasr, Reza Shokri, and Amir Houmansadr 
  Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning 
  In IEEE Symposium on Security and Privacy (S&P) -- Oakland, 2019.

• Milad Nasr, Reza Shokri, and Amir Houmansadr
  Machine Learning with Membership Privacy using Adversarial Regularization
  In the ACM Conference on Computer and Communications Security (CCS), 2018

• Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov
  Membership Inference Attacks against Machine Learning Models
  In IEEE Symposium on Security and Privacy (S&P) -- Oakland, 2017.

• Vincent Bindschaedler, Reza Shokri, and Carl Gunter
  Plausible Deniability for Privacy-Preserving Data Synthesis
  In the Proceedings of the VLDB Endowment International Conference on Very Large Data Bases (PVLDB), 2017.

• Vincent Bindschaedler and Reza Shokri.
  Synthesizing Plausible Privacy-Preserving Location Traces
  In IEEE Symposium on Security and Privacy (S&P) -- Oakland, 2016.

• Reza Shokri and Vitaly Shmatikov.
  Privacy-Preserving Deep Learning
  In ACM Conference on Computer and Communications Security (CCS), 2015.
  (Invited to) Annual Allerton Conference on Communication, Control, and Computing (Allerton) 2015

• Reza Shokri.
  Privacy Games: Optimal User-Centric Data Obfuscation
  In Privacy Enhancing Technologies Symposium (PETS), 2015

• Arthur Gervais, Reza Shokri, Adish Singla, Srdjan Capkun, and Vincent Lenders.
  Quantifying Web-Search Privacy [code]
  In ACM Conference on Computer and Communications Security (CCS), 2014.

• Reza Shokri, George Theodorakopoulos, Carmela Troncoso, Jean-Pierre Hubaux, and Jean-Yves Le Boudec.
  Protecting Location Privacy: Optimal Strategy against Localization Attacks
  In the 19th ACM Conference on Computer and Communications Security (CCS), 2012.

• Reza Shokri, George Theodorakopoulos, Jean-Yves Le Boudec, and Jean-Pierre Hubaux.
  Quantifying Location Privacy
  In IEEE Symposium on Security and Privacy (S&P) -- Oakland, 2011.

Awards & Honours

• NUS Early Career Research Award 2019 (Trustworthy Machine Learning for High-Dimensional Models)
• NUS Presidential Young Professorship 2019 (Privacy in Machine Learning)
• The Caspar Bowden Award for Outstanding Research in Privacy Enhancing Technologies 2018 (Privacy Risks of Machine Learning Models)
• Runner-up for the annual PET Award for Outstanding Research in Privacy Enhancing Technologies 2012 (Quantifying Location Privacy)

Teaching (2020/2021)

• CS3235: Computer Security
• CS6283: Topics in Computer Science: Logic in AI