PRIVÉ: Towards Privacy-Preserving Swarm Attestation
In modern large-scale systems comprising multiple heterogeneous devices, the introduction of swarm attestation schemes aims to alleviate the scalability and efficiency issues of traditional single-Prover and single-Verifier attestation. In this paper, we propose PRIVÉ, a privacy-preserving, scalable, and accountable swarm attestation scheme that addresses the limitations of existing solutions. Specifically, we eliminate the assumption of a trusted Verifier, which is not always applicable in real-world scenarios, as the need for the devices to share identifiable information with the Verifier may lead to the expansion of the attack landscape. To this end, we have designed an enhanced variant of the Direct Anonymous Attestation (DAA) protocol, offering traceability and linkability whenever needed. This enables PRIVÉ to achieve anonymous, privacy-preserving attestation while also providing the capability to trace a failed attestation back to the compromised device. To the best of our knowledge, this paper presents the first Universally Composable (UC) security model for swarm attestation accompanied by mathematical UC security proofs, as well as experimental benchmarking results that highlight the efficiency and scalability of the proposed scheme.
Authors
- Nada El Kassem (ORCID: https://orcid.org/0000-0002-2827-6493)
- Edlira Dushku (ORCID: https://orcid.org/0000-0002-4974-9739)
- Liqun Chen (ORCID: https://orcid.org/0000-0003-2680-4907)
- Dimitrios S. Karas
- Constantinos Patsakis (ORCID: https://orcid.org/0000-0002-4460-9331)
- Thanassis Giannetsos (ORCID: https://orcid.org/0000-0003-0663-2263)
- Ioannis Siachos (ORCID: https://orcid.org/0009-0003-2310-921X)
- Wouter Hellemans (ORCID: https://orcid.org/0000-0003-2344-044X)
- Stefanos Vasileiadis
Institutions
- University of Piraeus (GR)
- University of Surrey (GB)
- Ubitech (Greece) (GR)
- Aalborg University (DK)
- KU Leuven (BE)
Publication Details
- Published
- 2025-01-01
- DOI
- https://doi.org/10.5220/0013629000003979
- Primary Topic
- Advanced Malware Detection Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00