Reza SHOKRINUS Presidential Young Professor
PhD. (Computer Science, EPFL)
- Artificial Intelligence
- Algorithms & Theory
Research Sub Areas
- Data Privacy
- Trustworthy Machine Learning
- Federated Learning
Reza Shokri is a NUS Presidential Young Professor of Computer Science. His research focuses on data privacy and trustworthy machine learning. He is a recipient of the IEEE Security and Privacy (S&P) Test-of-Time Award 2021, for his paper on quantifying location privacy. 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. He received the NUS Early Career Research Award 2019, VMWare Early Career Faculty Award 2021, and Intel Faculty Research Award (Private AI Collaborative Research Institute) 2021. He obtained his PhD from EPFL.
- Hongyan Chang, and Reza Shokri. On the Privacy Risks of Algorithmic Fairness. IEEE European Symposium on Security and Privacy (EuroSP), 2021
- Reza Shokri, Martin Strobel, and Yair Zick On the Privacy Risks of Model Explanations AAAI/ACM Conference on AI, Ethics, and Society (AIES), 2021
- Sasi Kumar Murakonda, Reza Shokri, and George Theodorakopoulos Quantifying the Privacy Risks of Learning High-Dimensional Graphical Models International Conference on Artificial Intelligence and Statistics (AISTATS), 2021
- 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 HoumansadrMachine Learning with Membership Privacy using Adversarial RegularizationIn the ACM Conference on Computer and Communications Security CCS, 2018
- Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly ShmatikovMembership Inference Attacks against Machine Learning ModelsIn IEEE Symposium on Security and Privacy S&P -- Oakland, 2017.
- Vincent Bindschaedler, Reza Shokri, and Carl GunterPlausible Deniability for Privacy-Preserving Data SynthesisIn 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 TracesIn IEEE Symposium on Security and Privacy S&P -- Oakland, 2016.
- Reza Shokri and Vitaly Shmatikov.Privacy-Preserving Deep LearningIn 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 ObfuscationIn Privacy Enhancing Technologies Symposium PETS, 2015
- Arthur Gervais, Reza Shokri, Adish Singla, Srdjan Capkun, and Vincent Lenders.Quantifying Web-Search Privacy. 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 AttacksIn 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 PrivacyIn IEEE Symposium on Security and Privacy S&P -- Oakland, 2011.
Awards & Honours
- IEEE Security and Privacy (S&P) Test-of-Time Award 2021 (Quantifying Location Privacy)
- VMWare Early Career Faculty Award 2021 (Data Privacy and Trustworthy Machine Learning)
- Intel Faculty Research Award 2021 (Privacy-Preserving Federated Learning - Private AI Research Institute)
- 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)
- CS3235: Computer Security
- CS5562: Trustworthy Machine Learning