5 citations · 5 across the 6 of their papers we have counts for
7 papers
SLIP-SEC: Formalizing Secure Protocols for Model IP Protection
Racchit Jain, Satya Lokam, Yehonathan Refael +3
Large Language Models (LLMs) represent valuable intellectual property (IP), reflecting significant investments in training data, compute, and expertise. Deploying these models on p…
Unimodal Strategies in Density-Based Clustering
Oron Nir, Jay Tenenbaum, Ariel Shamir
Density-based clustering methods often surpass centroid-based counterparts, when addressing data with noise or arbitrary data distributions common in real-world problems. In this s…
SLIP: Securing LLMs IP Using Weights Decomposition
Yehonathan Refael, Adam Hakim, Lev Greenberg +6
Large language models (LLMs) have recently seen widespread adoption in both academia and industry. As these models grow, they become valuable intellectual property (IP), reflecting…
Concurrent Shuffle Differential Privacy Under Continual Observation
Jay Tenenbaum, Haim Kaplan, Yishay Mansour +1
We introduce the concurrent shuffle model of differential privacy. In this model we have multiple concurrent shufflers permuting messages from different, possibly overlapping, batc…
Differentially Private Multi-Armed Bandits in the Shuffle Model
Jay Tenenbaum, Haim Kaplan, Yishay Mansour +1
We give an -differentially private algorithm for the multi-armed bandit (MAB) problem in the shuffle model with a distribution-dependent regret of $O\left(\left(\s…
Locality Sensitive Hashing for Efficient Similar Polygon Retrieval
Haim Kaplan, Jay Tenenbaum
Locality Sensitive Hashing (LSH) is an effective method of indexing a set of items to support efficient nearest neighbors queries in high-dimensional spaces. The basic idea of LSH…
Locality Sensitive Hashing for Set-Queries, Motivated by Group Recommendations
Haim Kaplan, Jay Tenenbaum
Locality Sensitive Hashing (LSH) is an effective method to index a set of points such that we can efficiently find the nearest neighbors of a query point. We extend this method to…