My research is in data privacy and trustworthy machine learning. I am interested in designing algorithms to quantitatively measure the privacy risks of data processing algorithms, and build scalable algorithms for generalizable machine learning models that are privacy-preserving, robust, and interpretable. We work on understanding the trade-offs between different pillars of trust in machine learning for practical scenarios, and on resolving such conflicts with mathematical rigor. I have recieved the NUS Presidential Young Professorship, for working on privacy in machine learning, the NUS Early Carrier Research Award, for working on trustworthy machine learning for high-dimensional models, the AI Singapore research award (with Yair Zick), for working on privacy-aware transparency for machine learning, and AI Singapore research award (with Li Shiuan Peh), for working on efficient and secure collaborative machine learning.
➙ I have open positions for PhD students and postdoctoral researchers. Please send me your CV and research statement.
NEWS ➙ We have released our ML Privacy Meter tool, which enables quantifying the privacy risks of machine learning models.
ForMaL: DigiCosme Spring School on Formal Methods and Machine Learning, ENS Paris-Saclay, France, June 2019
EPFL Summer Research Institute, Switzerland, June 2019
INRIA Saclay and LIX, France, July 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
IETF Privacy Enhancements and Assessments Research Group, Singapore, November 2019
Keynote International Conference on Information Systems Security (ICISS), India, December 2019
Hongyan Chang, Virat Shejwalkar, Reza Shokri, and Amir Houmansadr
➙ Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge Transfer
arXiv:1912.11279, 2019
Reza Shokri, Martin Strobel, and Yair Zick
➙ Privacy Risks of Explaining Machine Learning Models
arXiv:1907.00164, 2019
Media:
Harvard Business Review
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.
➙ [talk by M. Nasr]
Sasi Kumar Murakonda, Reza Shokri, and George Theodorakopoulos
➙ Ultimate Power of Inference Attacks: Privacy Risks of Learning High-Dimensional Graphical Models
arXiv:1905.12774, 2019
Milad Nasr, Reza Shokri, and Amir Houmansadr
➙ Machine Learning with Membership Privacy using Adversarial Regularization
➙ [code]
➙ [talk by A. 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
arXiv:1803.05961, 2018
Media:
ZDNet
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.
Richard McPherson, Reza Shokri, and Vitaly Shmatikov
➙ Defeating Image Obfuscation with Deep Learning
arXiv:1609.00408, 2016
Media:
The Register,
WIRED,
The Telegraph,
BBC,
and more
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
Reza Shokri.
➙ 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.
Arthur Gervais, Reza Shokri, Adish Singla, Srdjan Capkun, and Vincent Lenders.
➙ Quantifying Web-Search Privacy
➙ [code]
ACM Conference on Computer and Communications Security (CCS), 2014.
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)
