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.

翻译：边缘计算通过将复杂处理任务卸载至边缘服务器，减轻了信息物理系统（CPS）中数据驱动控制的计算负担。然而，网络攻击日益复杂化，凸显了需要超越传统IT防护措施、针对CPS特有脆弱性的安全机制。本研究提出一种采用同态加密（如ElGamal和CKKS加密方案）的机密数据驱动增益调谐框架，以增强外包至外部服务器的增益调谐过程的网络安全。实现机密性FRIT的核心思路是将矩阵求逆运算替换为向量求和形式，从而允许应用同态运算。在128位安全强度下的数值算例表明，该方法在保持与传统方法相当性能的同时，为安全CPS选择合适加密方案提供了指导原则。