157 citations · 278 across the 8 of their papers we have counts for
8 papers
SynFi: Automatic Synthetic Fingerprint Generation
M. Sadegh Riazi, Seyed M. Chavoshian, Farinaz Koushanfar
Authentication and identification methods based on human fingerprints are ubiquitous in several systems ranging from government organizations to consumer products. The performance…
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…
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…
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…