Confidential FRIT via Homomorphic Encryption

TL;DR

This paper introduces a secure, homomorphic encryption-based framework for confidential gain-tuning in cyber-physical systems, enabling privacy-preserving computations on external servers without sacrificing performance.

Contribution

It proposes a novel homomorphic encryption approach for confidential FRIT, replacing matrix inversion with vector summation to ensure security in CPS gain-tuning.

Findings

Performance comparable to traditional methods under 128-bit security

Guidelines for selecting appropriate encryption schemes for CPS security

Enhanced cybersecurity in external server computations

Abstract

Edge computing alleviates the computation burden of data-driven control in cyber-physical systems (CPSs) by offloading complex processing to edge servers. However, the increasing sophistication of cyberattacks underscores the need for security measures that go beyond conventional IT protections and address the unique vulnerabilities of CPSs. This study proposes a confidential data-driven gain-tuning framework using homomorphic encryption, such as ElGamal and CKKS encryption schemes, to enhance cybersecurity in gain-tuning processes outsourced to external servers. The idea for realizing confidential FRIT is to replace the matrix inversion operation with a vector summation form, allowing homomorphic operations to be applied. Numerical examples under 128-bit security confirm performance comparable to conventional methods while providing guidelines for selecting suitable encryption schemes for secure CPS.