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Artemis: Decentralized, Secure, and Efficient Safety Monitoring with Dynamic Trajectories
Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology, HFUT), Ministry of Education, Hefei, China; School of Computer Science and Information Engineering, HFUT, Hefei, China.
Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology, HFUT), Ministry of Education, Hefei, China; School of Computer Science and Information Engineering, HFUT, Hefei, China.
Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology, HFUT), Ministry of Education, Hefei, China; School of Computer Science and Information Engineering, HFUT, Hefei, China.
Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology, HFUT), Ministry of Education, Hefei, China; School of Computer Science and Information Engineering, HFUT, Hefei, China.
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2026 (Engelska)Ingår i: Information and Communications Security: 27th International Conference, ICICS 2025, Nanjing, China, October 29–31, 2025, Proceedings, Part I / [ed] Jinguang Han; Yang Xiang; Guang Cheng; Willy Susilo; Liquan Chen, Springer, 2026, Vol. 16217, s. 387-404Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Secure safety monitoring is an intriguing and practical feature of current ride-hailing services. It enhances the riders safety after they are onboard a drivers vehicle while not violating location privacy. Existing work suffer from the problems of unnecessary trajectory uploading, centralized trajectory monitoring, redundant trajectory monitoring, and assuming static trajectories. In this work, we propose a decentralized, secure, and efficient safety monitoring scheme Artemis to guarantee rider safety while supporting dynamic trajectories, i.e., permitted trajectory deviations. In specific, we design a 2-out-of-n private threshold signature protocol to achieve trajectory authenticity, design a decentralized RHS platform and a secure trajectory similarity computation protocol to guarantee both efficiency and location privacy, and design a secure three-party computation protocol to ensure deviation authenticity. Formal security experiments and proofs are provided. Experimental results from extensive experiments based on Ethereum and Intel SGX2 demonstrate that Artemis is highly efficient.

Ort, förlag, år, upplaga, sidor
Springer, 2026. Vol. 16217, s. 387-404
Serie
Lecture Notes in Computer Science (LNCS), ISSN 0302-9743, E-ISSN 1611-3349 ; 16217
Nyckelord [en]
Ride-hailing services, Safety monitoring, Security, LocationPrivacy, Efficiency
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
URN: urn:nbn:se:oru:diva-128081DOI: 10.1007/978-981-95-3540-8_21ISI: 001688851200021ISBN: 9789819535392 (tryckt)ISBN: 9789819535408 (digital)OAI: oai:DiVA.org:oru-128081DiVA, id: diva2:2047886
Konferens
27th International Conference on Information and Communications Security-ICICS-Annual (ICICS 2025), Nanjing, China, October 29-31, 2025
Anmärkning

It is supported by the National Natural Science Foundation of China (NSFC) under the grant No. 62372149, No. U23A20303, and No. 62572168. It is supported by the Anhui Provincial Natural Science Foundation 2508085MF151. This research is supported by the Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China), under grant number No. BigKEOpen2025-04. It is partially supported by EU LOCARD Project under Grant H2020-SU-SEC-2018-832735.

Tillgänglig från: 2026-03-23 Skapad: 2026-03-23 Senast uppdaterad: 2026-03-23Bibliografiskt granskad

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Conti, Mauro

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