NUS Presidential Young Professor

Assistant Professor
CS Department, School of Computing
National University of Singapore (NUS)
Data Privacy and Trustworthy Machine Learning Research Lab

Twitter: @rzshokri
Phone: +65-651-64464
Office: COM2-03-60
Mailing Address: Dept. of Computer Science,
NUS School of Computing, 13 Computing Drive,
Computing 1, #03-27, Singapore 117417.

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, interpretable, and fair. 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 Career 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.

NEWS ➙ I am teaching a graduate course on Trustworthy Machine Learning. See for the list of research papers that we cover in the course (list curated jointly with Nicolas Papernot).

NEWS ➙ We have released our ML Privacy Meter tool, which enables quantifying the privacy risks of machine learning models. Here is our short article on Aiding Regulatory Compliance by Quantifying the Privacy Risks of Machine Learning, and its talk.

I have open positions for PhD students and postdoctoral researchers. Please send me your CV and research statement.

Selected Research Papers (see also Google Scholar and arXiv)

Anshul Aggarwal, Trevor Carlson, Reza Shokri, and Shruti Tople
SOTERIA: In Search of Efficient Neural Networks for Private Inference
arXiv:2007.12934, 2020.

Milad Nasr, Reza Shokri, and Amir Houmansadr
Improving Deep Learning with Differential Privacy using Gradient Encoding and Denoising
arXiv:2007.11524, 2020.

Hongyan Chang, Ta Duy Nguyen, Sasi Kumar Murakonda, Ehsan Kazemi, and Reza Shokri
On Adversarial Bias and the Robustness of Fair Machine Learning
arXiv:2006.08669, 2020.

Neel Patel, Reza Shokri, and Yair Zick
Model Explanations with Differential Privacy
arXiv:2006.09129, 2020.

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
On the Privacy Risks of Model Explanations
arXiv:1907.00164, 2019
Media: Harvard Business Review

Ni Trieu, Kareem Shehata, Prateek Saxena, Reza Shokri, and Dawn Song
Epione: Lightweight Contact Tracing with Strong Privacy
arXiv:2004.13293, 2020.

Te Juin Lester Tan, and Reza Shokri
Bypassing Backdoor Detection Algorithms in Deep Learning
IEEE European Symposium on Security and Privacy (EuroSP), 2020.

Congzheng Song, and Reza Shokri
Robust Membership Encoding: Inference Attacks and Copyright Protection for Deep Learning
ACM ASIA Conference on Computer and Communications Security (ASIACCS), 2020.

Liwei Song, Reza Shokri, and Prateek Mittal
Privacy Risks of Securing Machine Learning Models against Adversarial Examples [talk by L. Song]
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 [code] [talk by M. Nasr]
IEEE Symposium on Security and Privacy (S&P) -- Oakland, 2019.

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] [tool] [datasets] [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.
Federated Learning
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.


CS6283 (Sem 1: 2020-21): Topics in Computer Science: Trustworthy Machine Learning

CS3235 (Sem 1: 2020-21): Computer Security

CS3235 (Sem 2: 2019-20): Computer Security (secure channels, software security, OS security, privacy)

CS6231 (Sem 1: 2019-20): Topics in Computer Security: Adversarial Machine Learning (privacy, robustness)

CS4257 (Sem 2: 2018-19): Algorithmic Foundations of Privacy (anonymous communication, data privacy, private computation)

CS6231 (Sem 1: 2018-19): An Adversarial View of Privacy (inference attacks)

CS4257 (Sem 2: 2017-18): Algorithmic Foundations of Privacy (anonymous communication, data privacy, private computation)

CS6101 (Sem 1: 20**): Privacy and Security in Machine Learning (trustworthy machine learning)

Professional Activities

Organizer of NUS Computer Science Research Week: 2019, 2020

Program committee member of
  • IEEE Symposium on Security and Privacy (Oakland): 2019, 2020, 2021
  • International Joint Conferences on Artificial Intelligence (IJCAI): 2021
  • ACM Conference on Computer and Communications Security (CCS): 2017, 2019, 2020
  • Privacy-Enhancing Technologies Symposium (PETS): 2013, 2014, 2015, 2017, 2019, 2020
  • ACM ASIA Conference on Computer and Communications Security (ASIACCS): 2019, 2020
  • Deep Learning and Security workshop (DLS): 2020
  • AAAI Workshop on Privacy-Preserving Artificial Intelligence (PPAI): 2020
  • ACM CCS Workshop on Theory and Practice of Differential Privacy (TPDP): 2018, 2019
  • USENIX Security and AI Networking Conference: 2019
  • USENIX Security Symposium: 2015, 2016
  • Network and Distributed System Security Symposium (NDSS): 2016, 2017
  • IEEE European Symposium on Security and Privacy (Euro S&P): 2017
  • ACM Conference on Security and Privacy in Wireless and Mobile Networks (WiSec): 2014, 2015, 2016, 2018
  • Conference on Decision and Game Theory for Security (GameSec): 2015, 2016, 2018
  • International World Wide Web Conference (WWW): 2016
  • ACM Workshop on Privacy in the Electronic Society (WPES): 2012, 2015
  • ASIACCS Workshop on IoT Privacy, Trust, and Security (IoTPTS): 2015, 2016
  • Workshop on Understanding and Enhancing Online Privacy (UEOP): 2016
  • International Workshop on Obfuscation: Science, Technology, and Theory: 2017
  • International Conference on Privacy, Security and Trust (PST): 2014
Program co-chair of Hot Topics in Privacy Enhancing Technologies (HotPETs): 2013 and 2014

Recent Invited Talks and Visits


Hongyan Chang
(PhD Student)
Rishav Chourasia
(PhD Student)
Martin Strobel
(PhD Student)
Jiashu Tao
(PhD Student)
Neel Patel
(Research Assistant)
Ta Duy Nguyen
(Research Assistant)
Aadyaa Maddi
(Research Assistant)
(Undergrad Student): 2019/20
Mihir Khandekar
(Master's Student)
Anmin Kang
(Master's Student)
(Undergrad Student): 2019/20


Sasi Kumar Murakonda
(Research Assistant) 2018-20
Anshul Aggarwal
(Master's Student) 2019
(Research Assistant) 2020
Yong Ler Lee
(Undergrad Student): 2019/20
Qinghao Chu
(Undergrad Student): 2019/20
Guo Sheng Alexander Lee
(Undergrad Student) 2019/20
Te Juin Lester Tan
(Undergrad Student) 2018/19