The initial task is to compile and organize existing resources.

The main goal will be to create a "proof-of-concept" application that will train a machine learning classifier on encrypted images, run the classifier on additional encrypted images, and return the classifier's encrypted predictions.

• minkcoregame/privacy-CNN

  • Project description: This repository provides python code for privacy preserving image classification based on fully homomorphic encryption (FHE).

• shreyagarge/EncryptedImagePredictorNetwork

  • Project description: Implementation of a Neural Network that predicts encrypted handwritten digits from the MNIST dataset. Encryption and Network Implemented using Homomorphic encryption library SEAL. Model obtained by training on unencrypted images using TensorFlow.
  • License: GNU General Public License (GPL) v3.0

• fentec-project/neural-network-on-encrypted-data

  • Project description: Demonstrating neural network model applied on encrypted data by using functional encryption.
  • License: Apache License 2.0

• Homomorphic-Image-Encryption

  • Project description: Secure Outsourcing of Image Editing Based on Homomorphic Encryption
  • License: GNU General Public License (GPL) v3.0

• adhilaq/Homomorphic-Image-Encryption

  • Project description: Performance and Security Analysis on following Homomorphic Encryption Algorithms

• Computer-Security-homomorphic-image-encryption

  • Project description: Implemented 3 homomorphic image encryption algorithms- RSA, Pallier & Gentry(Full) in Python and verified their authenticity by doing various image processing operations on the encrypted image.

• Image-Encryption-based-on-Paillier-Cryptosystem

  • Project description: Image Encryption using Homomorphic Encryption Scheme

• Computing Arbitrary Functions of Encrypted Data. Craig Gentry. 2010. (PDF)

• Confidential Machine Learning on Untrusted Platforms: A Survey. Sagar Sharma, Keke Chen. 2020-12-15.

• A Systematic Comparison of Encrypted Machine Learning Solutions for Image Classification. Veneta Haralampieva, Daniel Rueckert, Jonathan Passerat-Palmbach. 2020-11-10.

• Efficient CNN Building Blocks for Encrypted Data. Nayna Jain, Karthik Nandakumar, Nalini Ratha, Sharath Pankanti, Uttam Kumar. 2021-01-30.

• On the Security of Homomorphic Encryption on Approximate Numbers. Baiyu Li, Daniele Micciancio. Received 2020-12-07, last revised 2021-03-07.

• CaRENets: Compact and Resource-Efficient CNN for Homomorphic Inference on Encrypted Medical Images. Jin Chao, Ahmad Al Badawi, Balagopal Unnikrishnan, Jie Lin, Chan Fook Mun, James M. Brown, J. Peter Campbell, Michael Chiang, Jayashree Kalpathy-Cramer, Vijay Ramaseshan Chandrasekhar, Pavitra Krishnaswamy, Khin Mi Mi Aung. 2019-01-29.

• CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy. Nathan Dowlin, Ran Gilad-Bachrach, Kim Laine, Kristin Lauter, Michael Naehrig, John Wernsing. Microsoft Research, Redmond, USA; Princeton University, New-Jersey, USA. 2016-02-24.

• Homomorphic Encryption Standardization

  • Description: an open consortium of industry, government and academia to standardize homomorphic encryption.

• Awesome Homomorphic Encryption

  • Desctiption: A curated list of amazing Homomorphic Encryption libraries, software and resources.

• CrypTen

  • Description: A research tool for secure machine learning in PyTorch. CrypTen currently implements a cryptographic method called secure multiparty computation (MPC), and we expect to add support for homomorphic encryption and secure enclaves in futue releases.
  • Website: https://crypten.ai/

• ZDNet: AI runs smack up against a big data problem in COVID-19 diagnosis (By Tiernan Ray, April 4, 2020)

  • Researchers around the world have quickly pulled together combinations of neural networks that show real promise in diagnosing COVID-19 from chest X-rays and CT scans. But a lack of data is hampering the ability of many efforts to move forward. Some kind of global data sharing may be the answer.