Implementation of Entropically Secure Encryption: Securing Personal Health Data

TL;DR

This paper presents the first implementation of Entropically Secure Encryption (ESE) for bulk data, optimizing polynomial multiplication for faster encryption of sensitive health data like X-ray images and genomes, with potential integration with Quantum Key Distribution.

Contribution

It introduces practical methods for implementing bulk ESE, including optimized polynomial multiplication and reduction algorithms, and evaluates its performance on health data use cases.

Findings

Implemented polynomial multiplication with improved speed

Achieved feasible encryption times for large health data

Discussed integration with Quantum Key Distribution

Abstract

Entropically Secure Encryption (ESE) offers unconditional security with shorter keys compared to the One-Time Pad. In this paper, we present the first implementation of ESE for bulk encryption. The main computational bottleneck for bulk ESE is a multiplication in a very large finite field. This involves multiplication of polynomials followed by modular reduction. We have implemented polynomial multiplication based on the gf2x library, with some modifications that avoid inputs of vastly different length, thus improving speed. Additionally, we have implemented a recently proposed efficient reduction algorithm that works for any polynomial degree. We investigate two use cases: X-ray images of patients and human genome data. We conduct entropy estimation using compression methods whose results determine the key lengths required for ESE. We report running times for all steps of the encryption. We discuss the potential of ESE to be used in conjunction with Quantum Key Distribution (QKD), in order to achieve full information-theoretic security of QKD-protected links for these use cases.