82 citations · 181 across the 16 of their papers we have counts for
3 papers · 1 filter
Differentially Private Learning of Markov Chains: Near-Optimal Sample Complexity for Item and User Level Privacy
Devansh Gupta, Cristóbal Guzman, Enayat Ullah
Sequential data such as documents, conversations, and trajectories are naturally partitioned across users, motivating privacy guarantees that protect each user's entire trajectory.…
Canaries in the Bank: Auditing User-Level Privacy in Private Evolution
Sai Aparna Aketi, Enayat Ullah, Shripad Gade
Private Evolution (PE) generates high-fidelity synthetic data in federated settings without exposing users' raw data. It aggregates clipped user votes over a shared candidate bank…
Efficient DP-SGD for LLMs with Randomized Clipping
Enayat Ullah, Sai Aparna Aketi, Devansh Gupta +2
Large language models (LLMs) are trained on vast datasets that may contain sensitive information. Differential privacy (DP), the de facto standard for formal privacy guarantees, pr…