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PeriodicalsArtificial Intelligence & Machine Learning

Secure Edge Data Auditing with Multiple Vendors and Servers

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Abstract & Overview

Mobile Edge Computing (MEC) has emerged as a key enabler for latency-sensitive applications by bringing computation and data storage closer to end users. Ensuring Edge Data Integrity (EDI) in MEC environments has thus become increasingly critical. While existing work primarily addresses Single-Vendor Multi-Server Edge Data Integrity (SVMS-EDI), practical MEC deployments often involve multiple application vendors (AVs) sharing edge servers. This multi-vendor setting introduces significant computation and communication overhead and weakens trust assumptions, as AVs cannot be fully trusted under the Multi-Vendor Multi-Server (MVMS) model. Consequently, robust solutions tailored to MVMS-EDI are needed. This paper presents MVMS-HMAC, a novel scheme that combines HMAC-based verification with distributed ledger to efficiently and securely address the MVMS-EDI problem. The scheme effectively mitigates key threats, including cheating by malicious AVs and forge, replace, and replay attacks from compromised edge servers. Through selective auditing, MVMS-HMAC minimizes computation and communication costs while preserving strong security guarantees. Theoretical analysis and experiments confirm its correctness, security, and efficiency, demonstrating clear advantages over existing approaches. Our work advances the field by providing a comprehensive MVMS-EDI problem model, a practical security framework, and an open-source implementation to support adoption.

Cite this record

Haojun Miao, Zifeng Qin, Fei Chen (2025). Secure Edge Data Auditing with Multiple Vendors and Servers. Journal of Machine Learning and Information Security, 1(1). https://doi.org/10.53941/jmlis.2025.100003

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Miao, H., Qin, Z., & Chen, F. (2025). Secure Edge Data Auditing with Multiple Vendors and Servers. Journal of Machine Learning and Information Security, 1(1). https://doi.org/10.53941/jmlis.2025.100003
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