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When Privacy Guarantees Meet Pre-Trained LLMs: A Case Study in Synthetic Data
Tuesday June 10, 2025 2:15pm - 2:30pm PDT
Santa Clara Ballroom
Yash Maurya and Aman Priyanshu, Carnegie Mellon University


Modern synthetic data generation with privacy guarantees has become increasingly prevalent. Take real data, create synthetic versions following similar patterns, and ensure privacy through differential privacy mechanisms. But what happens when theoretical privacy guarantees meet real-world data? Even with conservative epsilon values (ε
Speakers
avatar for Yash Maurya

Yash Maurya

Carnegie Mellon University
Yash Maurya is a Privacy Engineer who evaluates empirical guarantees of real-world privacy deployments, having designed privacy-preserving systems at Meta, PwC, BNY Mellon, and Samsung. An IAPP Westin Scholar with a Master's in Privacy Engineering from Carnegie Mellon University... Read More →
avatar for Aman Priyanshu

Aman Priyanshu

Carnegie Mellon University
Aman Priyanshu is an incoming AI Researcher at Cisco focused on AI safety & privacy. With a Masters from CMU, his research on foundation model vulnerabilities and LLM security has attracted media coverage and led to invitations to OpenAI's Red Teaming Network. His work has earned... Read More →
Tuesday June 10, 2025 2:15pm - 2:30pm PDT
Santa Clara Ballroom

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