157 citations · 278 across the 8 of their papers we have counts for
6 papers · 1 filter
HEAX: An Architecture for Computing on Encrypted Data
M. Sadegh Riazi, Kim Laine, Blake Pelton +1
With the rapid increase in cloud computing, concerns surrounding data privacy, security, and confidentiality also have been increased significantly. Not only cloud providers are su…
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…
ARM2GC: Succinct Garbled Processor for Secure Computation
Ebrahim M. Songhori, M. Sadegh Riazi, Siam U. Hussain +2
We present ARM2GC, a novel secure computation framework based on Yao's Garbled Circuit (GC) protocol and the ARM processor. It allows users to develop privacy-preserving applicatio…
Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications
M. Sadegh Riazi, Christian Weinert, Oleksandr Tkachenko +3
We present Chameleon, a novel hybrid (mixed-protocol) framework for secure function evaluation (SFE) which enables two parties to jointly compute a function without disclosing thei…
DeepSecure: Scalable Provably-Secure Deep Learning
Bita Darvish Rouhani, M. Sadegh Riazi, Farinaz Koushanfar
This paper proposes DeepSecure, a novel framework that enables scalable execution of the state-of-the-art Deep Learning (DL) models in a privacy-preserving setting. DeepSecure targ…
Sub-Linear Privacy-Preserving Near-Neighbor Search
M. Sadegh Riazi, Beidi Chen, Anshumali Shrivastava +2
In Near-Neighbor Search (NNS), a new client queries a database (held by a server) for the most similar data (near-neighbors) given a certain similarity metric. The Privacy-Preservi…