Lightweight integrity auditing of edge data for distributed edge computing scenarios

Abstract

With the increase of smart devices, edge computing is becoming a new computing paradigm that coexist with centralized cloud computing to process data distributed at the edge of the network. Based on this new paradigm, app vendors can store data replicas on geographically distributed edge servers to serve surrounding users to reduce access delay. However, since edge servers have limited storage space and computing ability, these edge data are vulnerable to various corruption. Therefore, how to efficiently audit the integrity of edge data has become an urgent problem to be solved. To tackle the Edge Data Integrity (EDI) problem, we propose a lightweight auditing scheme, namely EDI-SA. Firstly, inspired by the shuffle algorithm and the bucket sorting algorithm, we propose an improved sampling algorithm, which is a new lightweight challenge block sampling method. Secondly, based on algebraic signature, EDI-SA can achieve efficient aggregation verification and the signature computation cost is independent of the number of data blocks, which greatly reduces the computation overhead of app vendors and edge servers. Thirdly, EDI-SA supports batch auditing and provable dynamic update, app vendors can also uniquely locate the corrupted edge data. Finally, security and performance analyses demonstrate the security and effectiveness of EDI-SA.