Confidential FRIT via Homomorphic Encryption

2025-10-30

AI Summary

To address confidentiality threats arising from outsourcing data-driven gain tuning of Cyber-Physical Systems (CPS) to edge servers, this paper proposes a privacy-preserving framework based on homomorphic encryption. The method innovatively reformulates matrix inversion—central to the FRIT gain-tuning algorithm—into a vector summation form amenable to homomorphic evaluation, thereby overcoming the fundamental limitation that conventional matrix operations are not natively supported in homomorphic encryption schemes. Leveraging a hybrid ElGamal–CKKS cryptosystem, it enables secure vector-level homomorphic computation and control parameter optimization under 128-bit security. Experimental results demonstrate that the approach achieves computational overhead comparable to plaintext-based methods while guaranteeing end-to-end data confidentiality. Furthermore, it provides a scalable, practical guideline for selecting and composing homomorphic encryption schemes tailored to secure CPS deployments.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Machine Learning: Privacy
• Search and Optimization: Distributed Search

Application Category

• Security and Privacy: Applications of cryptography
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing

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.

Problem

• Securing cyber-physical systems against sophisticated cyberattacks
• Protecting outsourced gain-tuning processes using homomorphic encryption
• Enabling confidential FRIT by replacing matrix inversion with vector summation

Innovation

• Homomorphic encryption enables confidential data-driven control
• Vector summation replaces matrix inversion for encryption compatibility
• ElGamal and CKKS schemes provide 128-bit security