5 citations · 5 across the 3 of their papers we have counts for
4 papers
FALCON: Honest-Majority Maliciously Secure Framework for Private Deep Learning
Sameer Wagh, Shruti Tople, Fabrice Benhamouda +3
We propose Falcon, an end-to-end 3-party protocol for efficient private training and inference of large machine learning models. Falcon presents four main advantages - (i) It is hi…
Sub-logarithmic Distributed Oblivious RAM with Small Block Size
Eyal Kushilevitz, Tamer Mour
Oblivious RAM (ORAM) is a cryptographic primitive that allows a client to securely execute RAM programs over data that is stored in an untrusted server. Distributed Oblivious RAM i…
On the Power of Multiplexing in Number-on-the-Forehead Protocols
Eldar Aharoni, Eyal Kushilevitz
We study the direct-sum problem for -party ``Number On the Forehead'' (NOF) deterministic communication complexity. We prove several positive results, showing that the complexit…
Partition Arguments in Multiparty Communication Complexity
Jan Draisma, Eyal Kushilevitz, Enav Weinreb
Consider the "Number in Hand" multiparty communication complexity model, where k players holding inputs x_1,...,x_k in {0,1}^n communicate to compute the value f(x_1,...,x_k) of a…