A DES Homomorphic Scheme for Blackbox Data Analytics

Data confidentiality is a critical concern in corporate settings, particularly as data analysts seek to uncover trends and insights from sensitive datasets. Traditional approaches often require plaintext data, posing a significant risk to data privacy during analytics processes. In response to this challenge, homomorphic encryption schemes have emerged as a promising solution to enable secure data analytics while preserving confidentiality. In this work-in-progress paper, we present an ongoing implementation of a homomorphic scheme based on the Data Encryption Standard (DES) as a proof of concept. Our goal is to showcase how DES homomorphic encryption can facilitate secure data analytics by allowing computations on encrypted data without compromising confidentiality. While our work is still in the early stages, we aim to empower data custodians by enabling them to share complete cipher datasets with confidence, knowing that data analytics operations remain blackbox and preserve data availability. Overall, this work represents an ongoing effort to advance data security practices and contribute to the development of practical solutions for secure data analytics.