Abhradeep Thakurta
5 papers in the PaperMetrix corpus
Papers by this author
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Nearly-optimal private LASSO
2015 · Neural Information Processing Systems
We present a nearly optimal differentially private version of the well known LASSO estimator. Our algorithm provides privacy protection with respect to each training example. The excess risk of our algorithm, compared to the non-private …
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Encode, Shuffle, Analyze Privacy Revisited: Formalizations and Empirical Evaluation
2020 · arXiv (Cornell University)
Recently, a number of approaches and techniques have been introduced for reporting software statistics with strong privacy guarantees. These range from abstract algorithms to comprehensive systems with varying assumptions and built upon local differential privacy …
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Evading Curse of Dimensionality in Unconstrained Private GLMs via Private Gradient Descent
2020 · arXiv (Cornell University)
We revisit the well-studied problem of differentially private empirical risk minimization (ERM). We show that for unconstrained convex generalized linear models (GLMs), one can obtain an excess empirical risk of $\tilde O\left(\sqrt{\texttt{rank}}/εn\right)$, where ${\texttt{rank}}$ is …
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Privacy Amplification for Matrix Mechanisms
2023 · arXiv (Cornell University)
Privacy amplification exploits randomness in data selection to provide tighter differential privacy (DP) guarantees. This analysis is key to DP-SGD's success in machine learning, but, is not readily applicable to the newer state-of-the-art algorithms. This …
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The Attacker Moves Second: Stronger Adaptive Attacks Bypass Defenses Against Llm Jailbreaks and Prompt Injections
2025 · arXiv (Cornell University)
How should we evaluate the robustness of language model defenses? Current defenses against jailbreaks and prompt injections (which aim to prevent an attacker from eliciting harmful knowledge or remotely triggering malicious actions, respectively) are typically …