A review of homomorphic encryption and software tools for encrypted statistical machine learning · HackerPapers discussions

A review of homomorphic encryption and software tools for encrypted statistical machine learning — mentioned by proofofstake

Mon, 07 Aug 2017 18:08:59 GMT

They switched from structure-preserving encryption to neural encryption (using a neural net's layer activations). > Just a few months ago this package was released by Louis Aslett at Oxford http://www.louisaslett.com/HomomorphicEncryption/. Louis helped me use his package to do Fan and Vercauteren homomorphic encryption on my dataset. Because the ciphertexts are polynomials it's not too easy for an average data scientist to use the data. That's why I came up with more chill ways of encrypting Numerai's data that the article mentions like order-preserving encryption. There's a security vs easy…

Paper and discussion history. A mention is not an endorsement.

A review of homomorphic encryption and software tools for encrypted statistical machine learning — mentioned by BickNowstrom

Tue, 13 Dec 2016 02:01:10 GMT

""" https://arxiv.org/abs/1508.06574 "An encryption scheme is said to be homomorphic if certain mathematical operations can be applied directly to the cipher text in such a way that decrypting the result renders the same answer as applying the function to the original unencrypted data." The function = GradientBoostingRegressor the cipher text = X_encrypted original data = X same answer = mean absolute error """ import numpy as np from sklearn.metrics import mean_absolute_error from sklearn.ensemble import GradientBoostingRegressor # Replicability np.random.seed(0) # Create a data set with…

Paper and discussion history. A mention is not an endorsement.