Zobrazeno 1 - 10
of 524
pro vyhledávání: '"Riazi, M."'
Authentication and identification methods based on human fingerprints are ubiquitous in several systems ranging from government organizations to consumer products. The performance and reliability of such systems directly rely on the volume of data on
Externí odkaz:
http://arxiv.org/abs/2002.08900
Publikováno v:
Phys. Rev. E 102 (2020) 012310
Balance theory proposed by Heider for the first time modeled triplet interaction in a signed network, stating that relationships between two people, friendship or enmity, is dependent on a third person. The Hamiltonian of this model has an implicit a
Externí odkaz:
http://arxiv.org/abs/2001.01719
With the rapid increase in cloud computing, concerns surrounding data privacy, security, and confidentiality also have been increased significantly. Not only cloud providers are susceptible to internal and external hacks, but also in some scenarios,
Externí odkaz:
http://arxiv.org/abs/1909.09731
Autor:
Chen, Hao, Chillotti, Ilaria, Dong, Yihe, Poburinnaya, Oxana, Razenshteyn, Ilya, Riazi, M. Sadegh
The $k$-Nearest Neighbor Search ($k$-NNS) is the backbone of several cloud-based services such as recommender systems, face recognition, and database search on text and images. In these services, the client sends the query to the cloud server and rec
Externí odkaz:
http://arxiv.org/abs/1904.02033
Autor:
Riazi, M. Sadegh, Samragh, Mohammad, Chen, Hao, Laine, Kim, Lauter, Kristin, Koushanfar, Farinaz
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 learning model and send back the results. One standing challenge in this set
Externí odkaz:
http://arxiv.org/abs/1902.07342
Autor:
Songhori, Ebrahim M., Riazi, M. Sadegh, Hussain, Siam U., Sadeghi, Ahmad-Reza, Koushanfar, Farinaz
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 applications using standard high-level programming languages (e.g., C) and compi
Externí odkaz:
http://arxiv.org/abs/1902.02908
Autor:
Riazi, M. Sadegh, Weinert, Christian, Tkachenko, Oleksandr, Songhori, Ebrahim M., Schneider, Thomas, Koushanfar, Farinaz
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 their private inputs. Chameleon combines the best aspects of generic SFE p
Externí odkaz:
http://arxiv.org/abs/1801.03239
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 targets scenarios in which neither of the involved parties including the c
Externí odkaz:
http://arxiv.org/abs/1705.08963