Oblivious Online Monitoring for Safety LTL Specification via Fully Homomorphic Encryption
Ryotaro Banno, Kotaro Matsuoka, Naoki Matsumoto, Song Bian, Masaki Waga, Kohei Suenaga
摘要
Abstract In many Internet of Things (IoT) applications, data sensed by an IoT device are continuously sent to the server and monitored against a specification. Since the data often contain sensitive information, and the monitored specification is usually proprietary, both must be kept private from the other end. We propose a protocol to conduct oblivious online monitoring—online monitoring conducted without revealing the private information of each party to the other—against a safety LTL specification. In our protocol, we first convert a safety LTL formula into a DFA and conduct online monitoring with the DFA. Based on fully homomorphic encryption (FHE), we propose two online algorithms (Reverse and Block) to run a DFA obliviously. We prove the correctness and security of our entire protocol. We also show the scalability of our algorithms theoretically and empirically. Our case study shows that our algorithms are fast enough to monitor blood glucose levels online, demonstrating our protocol’s practical relevance.
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引用它的顶会 Paper3
- Differentially Private Runtime MonitoringBernd Finkbeiner, Frederik ScheererCAV 2026
- Privacy-Preserving Runtime VerificationThomas A. Henzinger, Mahyar Karimi, K. S. ThejaswiniCCS 2025
- Libra: Pattern-Scheduling Co-Optimization for Cross-Scheme FHE Code Generation over GPGPUSong Bian, Yintai Sun, Zian Zhao, Haowen Pan 等USENIX Security 2026
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