Meenatchi Sundaram Muthu Selva Annamalai

Orcid: 0000-0002-6452-9865

According to our database1, Meenatchi Sundaram Muthu Selva Annamalai authored at least 19 papers between 2021 and 2026.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

Online presence:

On csauthors.net:

Bibliography

2026
Tight Auditing of Differential Privacy in MST and AIM.
CoRR, April, 2026

CLIOPATRA: Extracting Private Information from LLM Insights.
CoRR, March, 2026

Secure bioinformatics: privacy-preserving federated analytics using homomorphic encryption.
Bioinform., 2026

To Shuffle or not to Shuffle: Auditing DP-SGD with Shuffling.
Proceedings of the 33rd Annual Network and Distributed System Security Symposium, 2026

2025
Beyond the Worst Case: Extending Differential Privacy Guarantees to Realistic Adversaries.
CoRR, July, 2025

The Hitchhiker's Guide to Efficient, End-to-End, and Tight DP Auditing.
CoRR, June, 2025

Understanding the Impact of Data Domain Extraction on Synthetic Data Privacy.
CoRR, April, 2025

The Elusive Pursuit of Reproducing PATE-GAN: Benchmarking, Auditing, Debugging.
Trans. Mach. Learn. Res., 2025

Beyond the Crawl: Unmasking Browser Fingerprinting in Real User Interactions.
Proceedings of the ACM on Web Conference 2025, 2025

The Importance of Being Discrete: Measuring the Impact of Discretization in End-to-End Differentially Private Synthetic Data.
Proceedings of the 2025 ACM SIGSAC Conference on Computer and Communications Security, 2025

2024
The Elusive Pursuit of Replicating PATE-GAN: Benchmarking, Auditing, Debugging.
CoRR, 2024

A Linear Reconstruction Approach for Attribute Inference Attacks against Synthetic Data.
Proceedings of the 33rd USENIX Security Symposium, 2024

"What do you want from theory alone?" Experimenting with Tight Auditing of Differentially Private Synthetic Data Generation.
Proceedings of the 33rd USENIX Security Symposium, 2024

Nearly Tight Black-Box Auditing of Differentially Private Machine Learning.
Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

FP-Fed: Privacy-Preserving Federated Detection of Browser Fingerprinting.
Proceedings of the 31st Annual Network and Distributed System Security Symposium, 2024

It's Our Loss: No Privacy Amplification for Hidden State DP-SGD With Non-Convex Loss.
Proceedings of the 2024 Workshop on Artificial Intelligence and Security, 2024

2022
Pool Inference Attacks on Local Differential Privacy: Quantifying the Privacy Guarantees of Apple's Count Mean Sketch in Practice.
Proceedings of the 31st USENIX Security Symposium, 2022

2021
Privacy-Preserving Collective Learning With Homomorphic Encryption.
IEEE Access, 2021

The Observatory of Anonymity: An Interactive Tool to Understand Re-Identification Risks in 89 countries.
Proceedings of the Companion of The Web Conference 2021, 2021


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