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Mobile edge computing (MEC) is an emerging technology to transform thecloud-based computing services into the edge-based ones. Autonomous vehicularnetwork (AVNET), as one of the most promising applications of MEC, can featureedge learning and communication techniques, improving the safety for autonomousvehicles (AVs). This paper focuses on the edge learning in AVNET, where AVs atthe edge of the network share model parameters instead of data in a distributedmanner, and an aggregator (e.g., a base station) aggregates parameters from AVsand at the end obtains a trained model. Despite promising, security issues,such as data leakage, computing integrity invasion and fault connection inexisting edge learning cases are not considered fully. To the best of ourknowledge, there lacks an effective scheme simultaneously covering theforegoing security issues. Therefore, we propose \textit{SecEL}, aprivacy-preserving, verifiable and fault-tolerant scheme for edge learning inAVNET. First, we leverage the primitive of bivariate polynomial-based secretsharing to encrypt model parameters by one-time padding. Second, we usehomomorphic authenticator based on message authentication code to supportverifiable computation. Third, we mitigate the computation failure problemcaused by fault connection. Last, we simulate and evaluate SecEL in terms oftime cost, throughput and classification accuracy. The experiment resultsdemonstrate the effectiveness of SecEL.

SecEL: Privacy-Preserving, Verifiable and Fault-Tolerant Edge Learning for Autonomous Vehicles