arXiv subjects

Jungjin Park

Publications and source records attributed to Jungjin Park.

4 recordsLinked to original sources

arXiv2026

Analysis of Dynamic-Key LWE-Based Encrypted Control Systems for Asymptotic Stability and Numerical Safety

This study analyzes dynamic-key Learning-with-Errors (LWE)-based encrypted state-feedback control systems with time-varying encoders and decoders. Using a Lyapunov-based approach, we derive conditions on the time-varying encoder and decoder parameters that ensure both asymptotic stability and numerical safety by preventing overflow. The validity of the derived conditions is confirmed through numerical examples.

eess.SY↗

arXiv2026

Dynamic-Key Post-Quantum Encrypted Control Against System Identification Attacks

This study proposes post-quantum encrypted control systems based on dynamic-key Learning with Errors (LWE) encryption schemes. The proposed method develops update maps that simultaneously update the private key and ciphertexts within the LWE framework, enabling dynamic-key encrypted control resistant to system identification attacks. The growth of errors induced by homomorphic operations is analyzed, and sufficient parameter conditions guaranteeing correct decryption at each control step are clarified. Furthermore, a design procedure for the encrypted control systems is presented based on security metrics such as sample-identifying complexity and deciphering time. A numerical example demonstrates that the proposed control systems achieve secure control against the considered system identification attack.

eess.SY↗

arXiv2025

Quantization and Security Parameter Design for Overflow-Free Confidential FRIT

This study proposes a systematic design procedure for determining the quantization gain and the security parameter in the Confidential Fictitious Reference Iterative Tuning (CFRIT), enabling overflow-free and accuracy-guaranteed encrypted controller tuning. Within an encrypted data-driven gain tuning, the range of quantization errors induced during the encoding (encryption) process can be estimated from operational data. Based on this insight, explicit analytical conditions on the quantization gain and the security parameter are derived to prevent overflow in computing over encrypted data. Furthermore, the analysis reveals a quantitative relationship between quantization-induced errors and the deviation between the gains obtained by CFRIT and non-confidential Fictitious Reference Iterative Tuning (FRIT), clarifying how parameter choice affects tuning accuracy. A numerical example verifies the proposed procedure by demonstrating that the designed parameters achieve accurate encrypted tuning within a prescribed tolerance while preventing overflow. In addition, the admissible region of parameter combinations is visualized to examine the characteristics of feasible and infeasible regions, providing practical insights into parameter design for encrypted data-driven control.

eess.SY↗

arXiv2025

Confidential FRIT via Homomorphic Encryption

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.

cs.CR↗