Reza Shokri

Orcid: 0000-0001-9816-0173

Affiliations:
  • National University of Singapore


According to our database1, Reza Shokri authored at least 87 papers between 2006 and 2024.

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Bibliography

2024
A Reconfigurable, Nonlinear, Low-Power, VCO-Based ADC for Neural Recording Applications.
Sensors, October, 2024

Context-Aware Membership Inference Attacks against Pre-trained Large Language Models.
CoRR, 2024

Range Membership Inference Attacks.
CoRR, 2024

Watermark Smoothing Attacks against Language Models.
CoRR, 2024

The Data Minimization Principle in Machine Learning.
CoRR, 2024

Efficient Privacy Auditing in Federated Learning.
Proceedings of the 33rd USENIX Security Symposium, 2024

Highly Linear, Digital OTA With Modified Input Stage.
Proceedings of the 19th Conference on Ph.D Research in Microelectronics and Electronics, 2024

Low-Cost High-Power Membership Inference Attacks.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Can LLMs Keep a Secret? Testing Privacy Implications of Language Models via Contextual Integrity Theory.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Leave-one-out Distinguishability in Machine Learning.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Smaller Language Models are Better Zero-shot Machine-Generated Text Detectors.
Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics, 2024

2023
Low-Cost High-Power Membership Inference by Boosting Relativity.
CoRR, 2023

Smaller Language Models are Better Black-box Machine-Generated Text Detectors.
CoRR, 2023

Unified Enhancement of Privacy Bounds for Mixture Mechanisms via f-Differential Privacy.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Initialization Matters: Privacy-Utility Analysis of Overparameterized Neural Networks.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Share Your Representation Only: Guaranteed Improvement of the Privacy-Utility Tradeoff in Federated Learning.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Bias Propagation in Federated Learning.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Multipolar Stimulator for DBS Application with Concurrent Imbalance Compensation.
Proceedings of the 30th IEEE International Conference on Electronics, Circuits and Systems, 2023

On The Impact of Machine Learning Randomness on Group Fairness.
Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, 2023

2022
Data Privacy and Trustworthy Machine Learning.
IEEE Secur. Priv., 2022

Forget Unlearning: Towards True Data-Deletion in Machine Learning.
CoRR, 2022

Differentially Private Learning Needs Hidden State (Or Much Faster Convergence).
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Model Explanations with Differential Privacy.
Proceedings of the FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea, June 21, 2022

What Does it Mean for a Language Model to Preserve Privacy?
Proceedings of the FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea, June 21, 2022

Quantifying Privacy Risks of Masked Language Models Using Membership Inference Attacks.
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022

Truth Serum: Poisoning Machine Learning Models to Reveal Their Secrets.
Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security, 2022

Enhanced Membership Inference Attacks against Machine Learning Models.
Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security, 2022

2021
Enhanced Membership Inference Attacks against Machine Learning Models.
CoRR, 2021

Differential Privacy Dynamics of Langevin Diffusion and Noisy Gradient Descent.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

On the Privacy Risks of Algorithmic Fairness.
Proceedings of the IEEE European Symposium on Security and Privacy, 2021

Quantifying the Privacy Risks of Learning High-Dimensional Graphical Models.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

On the Privacy Risks of Model Explanations.
Proceedings of the AIES '21: AAAI/ACM Conference on AI, 2021

2020
Epione: Lightweight Contact Tracing with Strong Privacy.
IEEE Data Eng. Bull., 2020

SOTERIA: In Search of Efficient Neural Networks for Private Inference.
CoRR, 2020

Improving Deep Learning with Differential Privacy using Gradient Encoding and Denoising.
CoRR, 2020

ML Privacy Meter: Aiding Regulatory Compliance by Quantifying the Privacy Risks of Machine Learning.
CoRR, 2020

On Adversarial Bias and the Robustness of Fair Machine Learning.
CoRR, 2020

Bypassing Backdoor Detection Algorithms in Deep Learning.
Proceedings of the IEEE European Symposium on Security and Privacy, 2020

Membership Encoding for Deep Learning.
Proceedings of the ASIA CCS '20: The 15th ACM Asia Conference on Computer and Communications Security, 2020

2019
Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge Transfer.
CoRR, 2019

Privacy Risks of Explaining Machine Learning Models.
CoRR, 2019

Ultimate Power of Inference Attacks: Privacy Risks of High-Dimensional Models.
CoRR, 2019

Membership Inference Attacks Against Adversarially Robust Deep Learning Models.
Proceedings of the 2019 IEEE Security and Privacy Workshops, 2019

Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning.
Proceedings of the 2019 IEEE Symposium on Security and Privacy, 2019

Trusting Machine Learning: Privacy, Robustness, and Transparency Challenges.
Proceedings of the ACM Workshop on Information Hiding and Multimedia Security, 2019

Privacy Risks of Securing Machine Learning Models against Adversarial Examples.
Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, 2019

2018
A Predictive Model for User Motivation and Utility Implications of Privacy-Protection Mechanisms in Location Check-Ins.
IEEE Trans. Mob. Comput., 2018

Comprehensive Privacy Analysis of Deep Learning: Stand-alone and Federated Learning under Passive and Active White-box Inference Attacks.
CoRR, 2018

Chiron: Privacy-preserving Machine Learning as a Service.
CoRR, 2018

A Non-Parametric Generative Model for Human Trajectories.
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

Machine Learning with Membership Privacy using Adversarial Regularization.
Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, 2018

2017
Quantifying Interdependent Privacy Risks with Location Data.
IEEE Trans. Mob. Comput., 2017

Privacy Games Along Location Traces: A Game-Theoretic Framework for Optimizing Location Privacy.
ACM Trans. Priv. Secur., 2017

Plausible Deniability for Privacy-Preserving Data Synthesis.
Proc. VLDB Endow., 2017

Membership Inference Attacks Against Machine Learning Models.
Proceedings of the 2017 IEEE Symposium on Security and Privacy, 2017

2016
Membership Inference Attacks against Machine Learning Models.
CoRR, 2016

Defeating Image Obfuscation with Deep Learning.
CoRR, 2016

Synthesizing Plausible Privacy-Preserving Location Traces.
Proceedings of the IEEE Symposium on Security and Privacy, 2016

2015
Privacy Games: Optimal User-Centric Data Obfuscation.
Proc. Priv. Enhancing Technol., 2015

Quantifying and protecting location privacy.
it Inf. Technol., 2015

Privacy through Fake yet Semantically Real Traces.
CoRR, 2015

Predicting Users' Motivations behind Location Check-Ins and Utility Implications of Privacy Protection Mechanisms.
Proceedings of the 22nd Annual Network and Distributed System Security Symposium, 2015

Privacy-Preserving Deep Learning.
Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, 2015

2014
Hiding in the Mobile Crowd: LocationPrivacy through Collaboration.
IEEE Trans. Dependable Secur. Comput., 2014

Privacy Games: Optimal Protection Mechanism Design for Bayesian and Differential Privacy.
CoRR, 2014

Prolonging the Hide-and-Seek Game: Optimal Trajectory Privacy for Location-Based Services.
Proceedings of the 13th Workshop on Privacy in the Electronic Society, 2014

Quantifying the Effect of Co-location Information on Location Privacy.
Proceedings of the Privacy Enhancing Technologies - 14th International Symposium, 2014

Quantifying Web-Search Privacy.
Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security, 2014

2013
Quantifying and Protecting Location Privacy.
PhD thesis, 2013

Quantifizierung und Schutz von Location Privacy.
Proceedings of the Ausgezeichnete Informatikdissertationen 2013, 2013

2012
Protecting location privacy: optimal strategy against localization attacks.
Proceedings of the ACM Conference on Computer and Communications Security, 2012

2011
Quantifying Location Privacy.
Proceedings of the 32nd IEEE Symposium on Security and Privacy, 2011

Quantifying Location Privacy: The Case of Sporadic Location Exposure.
Proceedings of the Privacy Enhancing Technologies - 11th International Symposium, 2011

Collaborative Location Privacy.
Proceedings of the IEEE 8th International Conference on Mobile Adhoc and Sensor Systems, 2011

Collaborative Location Privacy with Rational Users.
Proceedings of the Decision and Game Theory for Security - Second International Conference, 2011

Evaluating the Privacy Risk of Location-Based Services.
Proceedings of the Financial Cryptography and Data Security, 2011

2010
Unraveling an old cloak: k-anonymity for location privacy.
Proceedings of the 2010 ACM Workshop on Privacy in the Electronic Society, 2010

On the tradeoff between trust and privacy in wireless ad hoc networks.
Proceedings of the Third ACM Conference on Wireless Network Security, 2010

2009
A distortion-based metric for location privacy.
Proceedings of the 2009 ACM Workshop on Privacy in the Electronic Society, 2009

A practical secure neighbor verification protocol for wireless sensor networks.
Proceedings of the Second ACM Conference on Wireless Network Security, 2009

Preserving privacy in collaborative filtering through distributed aggregation of offline profiles.
Proceedings of the 2009 ACM Conference on Recommender Systems, 2009

On the Optimal Placement of Mix Zones.
Proceedings of the Privacy Enhancing Technologies, 9th International Symposium, 2009

2008
AntMig: A Novel Code Migration Method to Conserve Energy in Wireless Sensor Networks.
Proceedings of the WCNC 2008, IEEE Wireless Communications & Networking Conference, March 31 2008, 2008

2007
Efficient and Adjustable Recipient Anonymity in Mobile Ad Hoc Networks.
Proceedings of the IEEE 4th International Conference on Mobile Adhoc and Sensor Systems, 2007

Anonymous Routing in MANET Using Random Identifiers.
Proceedings of the Sixth International Conference on Networking (ICN 2007), 2007

Chain-Based Anonymous Routing for Wireless Ad Hoc Networks.
Proceedings of the 4th IEEE Consumer Communications and Networking Conference, 2007

2006
DDPM: Dynamic Deterministic Packet Marking for IP Traceback.
Proceedings of the 14th IEEE International Conference on Networks, 2006


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