Tricks for Accelerating (encrypted) Prediction As a Service
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Updated
May 28, 2019 - HTML
Tricks for Accelerating (encrypted) Prediction As a Service
This repository contains personal notes and summaries on Secure and Private AI
Secure Linear Regression in the Semi-Honest Two-Party Setting.
Crypto-Convolutional Neural Network library written on top of SEAL 2.3.1
Python Privacy framework
privacy preserving recommendation system research project as research engineer of https://www.openmined.org community
This repository contains all the implementation of different papers on Federated Learning
PyTorch implementation of NoPeekNN
Full stack service enabling decentralized machine learning on private data
Data anonymization
A project to simulate various differential privacy scenarios using OpenDp.
Privacy-Preserving Multi-task Learning - Paper published at 2018 IEEE ICDM. Reference - K. Liu, N. Uplavikar, W. Jiang and Y. Fu, "Privacy-Preserving Multi-task Learning," 2018 IEEE International Conference on Data Mining (ICDM), Singapore, 2018, pp. 1128-1133, doi: 10.1109/ICDM.2018.00147.
Implementation for 'Interpretable Complex-Valued Neural Networks for Privacy Protection'
Privacy Preserving Convolutional Neural Network using Homomorphic Encryption for secure inference
A port of the tensorflow-lite for microcontrollers framework to Intel's SGX Framework. Designed to simplify research of privacy preserving machine learning in the context of trusted execution environments (TEEs).
Privacy-preserving federated learning is distributed machine learning where multiple collaborators train a model through protected gradients. To achieve robustness to users dropping out, existing practical privacy-preserving federated learning schemes are based on (t, N)-threshold secret sharing. Such schemes rely on a strong assumption to guara…
Bachelor's Thesis in Computer Science: Privacy-Preserving Federated Learning Applied to Decentralized Data
Understanding the Tradeoffs in Client-side Privacy for Downstream Speech Tasks
A crypto-assisted framework for protecting the privacy of models and queries in inference.
PRICURE: Privacy-Preserving Collaborative Inference in a Multi-Party Setting
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