DOCSLIB.ORG
Explore
Sign Up
Log In
Upload
Search
Home
» Tags
» Microsoft SEAL
Microsoft SEAL
ARTIFICIAL INTELLIGENCE and ALGORITHMIC LIABILITY a Technology and Risk Engineering Perspective from Zurich Insurance Group and Microsoft Corp
Practical Homomorphic Encryption and Cryptanalysis
Information Guide
Homomorphic Encryption Library
Arxiv:2102.00319V1 [Cs.CR] 30 Jan 2021
MP2ML: a Mixed-Protocol Machine Learning Framework for Private Inference∗ (Full Version)
Smithsonian Institution Fiscal Year 2021 Budget Justification to Congress
DELPHI: a Cryptographic Inference Service for Neural Networks Pratyush Mishra Ryan Lehmkuhl Akshayaram Srinivasan Wenting Zheng Raluca Ada Popa
Getting the Most from Your Schmidt Cassegrain Or Any Catadioptric Telescope
Advances and Open Problems in Federated Learning
Friday, November 6, 2020 Home-Delivered $1.90, Retail $2.20
Partially Encrypted Machine Learning Using Functional Encryption
Practical MPC+FHE with Applications in Secure Multi-Party Neural Network Evaluation Ruiyu Zhu Changchang Ding Yan Huang Indiana University, Bloomington
Homomorphic Encryption for Machine Learning in Medicine and Bioinformatics
Multiparty Homomorphic Encryption from Ring-Learning-With-Errors
Final Copy 2021 06 24 Foyer
Combining Bayesian Deep Learning and Homomorphic
Sok: Fully Homomorphic Encryption Compilers
Top View
Private Outsourced Translation for Medical Data
SEAL-Embedded: a Homomorphic Encryption Library for the Internet of Things
New Art/Science Affinities
Multiparty Homomorphic Encryption from Ring-Learning-With-Errors
CHET: an Optimizing Compiler for Fully-Homomorphic Neural-Network Inferencing
UNIVERSITY of CALIFORNIA SAN DIEGO Efficient Learning In
Barrier Truth – Saturday 24Th July 2021
Privacy-Preserving Web Page Classification Via Fully Homomorphic Encryption Edward Chou*, Arun Gururajan†, Kim Laine††, Nitin Kumar Goel†, Anna Bertiger†, Jack W
Hereby Granted, Provided That the Above Copyright Notice and This Permission Notice Appear in All Copies
Securegbm: Secure Multi-Party Gradient Boosting
An Extensible Programming Language and Theorem Prover
A Mixed-Protocol Machine Learning Framework for Private Inference (Extended Abstract)∗
New Directions in Efficient Privacypreserving Machine Learning
Improved Security for a Ring-Based Fully Homomorphic Encryption
Happykidz: Privacy Preserving Phone Usage Tracking
Privacy-Preserving Biomedical Data Sharing and Computation
Finding the Shape of Space
Provably Secure Delegable Blockchain-Based Decision-Making
Advances and Open Problems in Federated Learning Full Text Available At
Introduction to FHE and the TFHE Scheme
On the Explanation and Implementation of Three Open-Source Fully Homomorphic Encryption Libraries
Security Enhancement and Privacy Protection for Biometric Systems, C Juli 2021 Supervisors: Prof
FOR RESEARCH and STUDY 2017 | 1 I Honor You for Your Commitment to Increasing Your Knowledge and Diffusing It to Others
Advances and Open Problems in Federated Learning
HEAX: an Architecture for Computing on Encrypted Data