82 citations · 181 across the 16 of their papers we have counts for
18 papers
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…
Synthetic Tabular Data: Methods, Attacks and Defenses
Graham Cormode, Samuel Maddock, Enayat Ullah +1
Synthetic data is often positioned as a solution to replace sensitive fixed-size datasets with a source of unlimited matching data, freed from privacy concerns. There has been much…
Public-data Assisted Private Stochastic Optimization: Power and Limitations
Enayat Ullah, Michael Menart, Raef Bassily +2
We study the limits and capability of public-data assisted differentially private (PA-DP) algorithms. Specifically, we focus on the problem of stochastic convex optimization (SCO)…
Differentially Private Non-Convex Optimization under the KL Condition with Optimal Rates
Michael Menart, Enayat Ullah, Raman Arora +2
We study private empirical risk minimization (ERM) problem for losses satisfying the -Kurdyka-Łojasiewicz (KL) condition. The Polyak-Łojasiewicz (PL) condition is a special…