My research is in computer security and privacy. My current focus is on trustworthy and privacy-preserving computation (in machine learning).
➙ I have open positions for PhD students, postdoctoral researchers, long-term interns, and NUS undergraduate/master students. Please send me your CV and research statement. Internship applicants need to fill out this form.
ForMaL: DigiCosme Spring School on Formal Methods and Machine Learning, ENS Paris-Saclay, France, June 2019
EPFL Summer Research Institute, Switzerland, June 2019
Keynote ACM Workshop on Information Hiding and Multimedia Security (IH&MMSec), Paris, France, July 2019
INRIA Grenoble, France, July 2019
AI Singapore Summer School, July 2019
Keynote International Conference on Information Systems Security (ICISS), India, December 2019
Reza Shokri, Martin Strobel, Yair Zick
➙ Privacy Risks of Explaining Machine Learning Models
Sasi Kumar Murakonda, Reza Shokri, George Theodorakopoulos
➙ Ultimate Power of Inference Attacks: Privacy Risks of High-Dimensional Models
Te Juin Lester Tan, Reza Shokri
➙ Bypassing Backdoor Detection Algorithms in Deep Learning
Liwei Song, Reza Shokri, and Prateek Mittal
➙ Privacy Risks of Securing Machine Learning Models against Adversarial Examples
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
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 ➙ [code] ➙ [talk by Amir Houmansadr]
ACM Conference on Computer and Communications Security (CCS), 2018.
Tyler Hunt, Congzheng Song, Reza Shokri, Vitaly Shmatikov, and Emmett Witchel
➙ Chiron: Privacy-preserving Machine Learning as a Service
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov
➙ Membership Inference Attacks against Machine Learning Models ➙ [code] ➙ [data] ➙ [talk]
IEEE Symposium on Security and Privacy (S&P) -- Oakland, 2017.
The Caspar Bowden Award for Outstanding Research in Privacy Enhancing Technologies 2018.
Vincent Bindschaedler, Reza Shokri, and Carl Gunter
➙ Plausible Deniability for Privacy-Preserving Data Synthesis ➙ [code]
VLDB Endowment International Conference on Very Large Data Bases (PVLDB), 2017.
Vincent Bindschaedler and Reza Shokri.
➙ Synthesizing Plausible Privacy-Preserving Location Traces ➙ [code] ➙ [talk by V. Bindschaedler]
IEEE Symposium on Security and Privacy (S&P) -- Oakland, 2016.
Reza Shokri, George Theodorakopoulos, and Carmela Troncoso
➙ Privacy Games along Location Traces: A Game-Theoretic Framework for Optimizing Location Privacy
ACM Transactions on Privacy and Security (TOPS), 2016.
Reza Shokri and Vitaly Shmatikov.
➙ Privacy-Preserving Deep Learning ➙ [code]
ACM Conference on Computer and Communications Security (CCS), 2015.
(Invited to) Conference on Communication, Control, and Computing (Allerton), 2015.
Media: MIT Technology Review
➙ Privacy Games: Optimal User-Centric Data Obfuscation
Privacy Enhancing Technologies Symposium (PETS), 2015
Igor Bilogrevic, Kevin Huguenin, Stephan Mihaila, Reza Shokri, and Jean-Pierre Hubaux.
➙ Predicting Users' Motivations behind Location Check-Ins and Utility Implications of Privacy Protection Mechanisms
Network and Distributed System Security (NDSS) Symposium, 2015.
Reza Shokri, George Theodorakopoulos, Panos Papadimitratos, Ehsan Kazemi, and Jean-Pierre Hubaux.
➙ Hiding in the Mobile Crowd: Location Privacy through Collaboration
IEEE Transactions on Dependable and Secure Computing (TDSC), 2014.
Reza Shokri, George Theodorakopoulos, Carmela Troncoso, Jean-Pierre Hubaux, and Jean-Yves Le Boudec.
➙ Protecting Location Privacy: Optimal Strategy against Localization Attacks ➙ [code]
ACM Conference on Computer and Communications Security (CCS), 2012.
Reza Shokri, George Theodorakopoulos, Jean-Yves Le Boudec, and Jean-Pierre Hubaux.
➙ Quantifying Location Privacy ➙ [code]
IEEE Symposium on Security and Privacy (S&P) -- Oakland, 2011.
Runner-up for the Outstanding Research Award in Privacy Enhancing Technologies 2012.
CS6231 (Sem 1: 2019-20): Adversarial Machine Learning
CS3235 (Sem 2: 2019-20): Computer Security
CS4257 (Sem 2: 2017-18, 2018-19): Algorithmic Foundations of Privacy (anonymous communication, data privacy, private computation)
CS6231 (Sem 1: 2018-19): An Adversarial View of Privacy (inference attacks)
CS6101 (Sem 1: 20**): Privacy and Security in Machine Learning (adversarial machine learning)