activity
20172021
most citedXONN: XNOR-based Oblivious Deep Neural Network Inference

157 citations · 205 across the 8 of their papers we have counts for

collaborators

8 papers

cs.IT2021

Algebraic Geometric Secret Sharing Schemes over Large Fields Are Asymptotically Threshold

Fan Peng, Hao Chen, Chang-An Zhao

In Chen-Cramer Crypto 2006 paper \cite{cc} algebraic geometric secret sharing schemes were proposed such that the "Fundamental Theorem in Information-Theoretically Secure Multipart…

cs.CR2019★ 15 cited

Toward Scalable Fully Homomorphic Encryption Through Light Trusted Computing Assistance

Wenhao Wang, Yichen Jiang, Qintao Shen +8

It has been a long standing problem to securely outsource computation tasks to an untrusted party with integrity and confidentiality guarantees. While fully homomorphic encryption…

cs.DS2019★ 12 cited

SANNS: Scaling Up Secure Approximate k-Nearest Neighbors Search

Hao Chen, Ilaria Chillotti, Yihe Dong +3

The -Nearest Neighbor Search (-NNS) is the backbone of several cloud-based services such as recommender systems, face recognition, and database search on text and images. In…

cs.CR2019★ 157 cited

XONN: XNOR-based Oblivious Deep Neural Network Inference

M. Sadegh Riazi, Mohammad Samragh, Hao Chen +3

Advancements in deep learning enable cloud servers to provide inference-as-a-service for clients. In this scenario, clients send their raw data to the server to run the deep learni…

cs.LG2018★ 9 cited

CHET: Compiler and Runtime for Homomorphic Evaluation of Tensor Programs

Roshan Dathathri, Olli Saarikivi, Hao Chen +5

Fully Homomorphic Encryption (FHE) refers to a set of encryption schemes that allow computations to be applied directly on encrypted data without requiring a secret key. This enabl…

cs.DS2017★ 3 cited

Optimizing relinearization in circuits for homomorphic encryption

Hao Chen

Fully homomorphic encryption (FHE) allows an untrusted party to evaluate arithmetic cir- cuits, i.e., perform additions and multiplications on encrypted data, without having the de…