Practical Privacy-Preserving Data Science With Homomorphic Encryption: An Overview
This survey addresses the critical need for privacy-preserving data analysis for enterprises and scientific communities dealing with confidential data, offering a pathway to leverage cloud computing and collaboration without privacy risks.
This paper provides an overview of Homomorphic Encryption (HE) and its application in privacy-preserving data science, addressing the challenge of analyzing confidential data without compromising privacy. It explores how HE enables computations on encrypted data, preserving data features and format, and discusses its potential for enterprise applications, specifically citing use cases for the Central Bank of Italy.
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.