Homomorphic Encryption

Homomorphic Encryption refers to a new type of encryption technology that allows computation to be directly on encrypted data, without requiring any decryption in the process.

The first homomorphic encryption scheme was invented in 2009 and several improved schemes were created over the following years. There were a few notable and publicly available implementations, but their use required extensive understanding of the complicated mathematics underlying homomorphic encryption and were not easily usable by normal software developers.

But Why?

Cheap cloud computing and cloud storage have fundamentally changed how businesses and individuals use and manage their data.

Traditional encryption methods, such as AES, are extremely fast, and allow data to be stored conveniently in encrypted form. However, to perform even simple analytics on the encrypted data, either the cloud server needs access to the secret key, which leads to security concerns, or the owner of the data needs to download, decrypt, and operate on the data locally, which can be costly and create a logistic challenge.

Homomorphic encryption can be used to simplify this scenario considerably, as the cloud can directly operate on the encrypted data, and return only the encrypted result to the owner of the data. More complex application scenarios can involve multiple parties with private data that a third party can operate on, and return the result to one or more of the participants to be decrypted.

For More info and news look here

Microsoft Open-Sources Its SEAL Encryption Technology Allowing Computations on Encrypted Data
CardRates | January 31, 2019

Intel, Microsoft Push Homomorphic Encryption with Open-Source Moves
Toolbox | January 10, 2019

Microsoft Open Sources Homomorphic Encryption Library “SEAL”
Computer Business Review | January 2, 2019

SEAL up your data just like Microsoft: Redmond open-sources ‘simple’ homomorphic encryption blueprints
The Register | December 4, 2018

Microsoft Open Sources SEAL Homomorphic Encryption Library
Decipher | December 3, 2018

HE-Transformer for nGraph: Enabling Deep Learning on Encrypted Data
Intel AI | December 3, 2018