Practical Privacy-Preserving Data Science With Homomorphic Encryption: An Overview

TL;DR

This paper reviews how homomorphic encryption enables privacy-preserving data science, allowing computations on encrypted data to facilitate secure enterprise applications and collaboration without compromising sensitive information.

Contribution

It provides a comprehensive survey of homomorphic encryption techniques and recent advances in integrating HE with data science for privacy-preserving applications.

Findings

Homomorphic encryption allows computation on encrypted data without decryption.

Recent advances improve efficiency and applicability of HE in data science.

Use cases include privacy-preserving enterprise applications for central banks.

Abstract

Privacy has gained a growing interest nowadays due to the increasing and unmanageable amount of produced confidential data. Concerns about the possibility of sharing data with third parties, to gain fruitful insights, beset enterprise environments; value not only resides in data but also in the intellectual property of algorithms and models that offer analysis results. This impasse locks both the availability of high-performance computing resources in the "as-a-service" paradigm and the exchange of knowledge with the scientific community in a collaborative view. Privacy-preserving data science enables the use of private data and algorithms without putting at risk their privacy. Conventional encryption schemes are not able to work on encrypted data without decrypting them first. Homomorphic Encryption (HE) is a form of encryption that allows the computation of encrypted data while preserving the features and the format of the plaintext. Against the background of interesting use cases for the Central Bank of Italy, this article focuses on how HE and data science can be leveraged for the design and development of privacy-preserving enterprise applications. We propose a survey of main Homomorphic Encryption techniques and recent advances in the conubium between data science and HE.