6 papers
Wally: Batched Private Nearest Neighbor Search at Scale
Hilal Asi, Fabian Boemer, Nicholas Genise +8
We present Wally, a batched private nearest-neighbor search protocol that uses differential privacy to break the linear computation barrier of fully-oblivious schemes. In Tiptoe, t…
Models That Prove Their Own Correctness
Noga Amit, Shafi Goldwasser, Orr Paradise +1
How can we trust the correctness of a learned model on a particular input of interest? Model accuracy is typically measured on average over a distribution of inputs, giving no guar…
PREAMBLE: Private and Efficient Aggregation via Block Sparse Vectors
Hilal Asi, Vitaly Feldman, Hannah Keller +2
We revisit the problem of secure aggregation of high-dimensional vectors in a two-server system such as Prio. These systems are typically used to aggregate vectors such as gradient…
On the Impossibility of Separating Intelligence from Judgment: The Computational Intractability of Filtering for AI Alignment
Sarah Ball, Greg Gluch, Shafi Goldwasser +3
With the increased deployment of large language models (LLMs), one concern is their potential misuse for generating harmful content. Our work studies the alignment challenge, with…
Accuracy vs. Accuracy: Computational Tradeoffs Between Classification Rates and Utility
Noga Amit, Omer Reingold, Guy N. Rothblum
We revisit the foundations of fairness and its interplay with utility and efficiency in settings where the training data contain richer labels, such as individual types, rankings,…
Local Pan-Privacy for Federated Analytics
Vitaly Feldman, Audra McMillan, Guy N. Rothblum +1
Pan-privacy was proposed by Dwork et al. as an approach to designing a private analytics system that retains its privacy properties in the face of intrusions that expose the system…