Practical applications of homomorphic encryption

Abstract

With the rush of advances in solutions for homomorphic encryption, the promise and hype grows. Homomorphic encryption offers the promise of allowing the user to upload encrypted data to the cloud, which the cloud can then operate on without having the secret key. The cloud can return encrypted outputs of computations to the user without ever decrypting the data, thus providing hosting of data and services without compromising privacy. The catch is the degradation of performance and issues of scalability and flexibility. This talk will survey the current state of the art and the trade-offs when using homomorphic encryption, and highlight scenarios and functionality where homomorphic encryption seems to be the most appropriate solution. In particular, homomorphic encryption can be used to enable private versions of some basic machine learning algorithms. This talk will cover several pieces of joint work with Michael Naehrig, Vinod Vaikuntanathan, and Thore Graepel.