A coding computation scheme for secure aggregation

Abstract

Data aggregation involves the integration of relevant data generated across platforms and devices, leveraging the potential value of sensory data. However, in addition to security and efficiency, which are the basic requirements for data aggregation involving private data, how to achieve fault tolerance and interference of aggregation in real computing networks is imminent and is the main contribution of this paper. In this paper, we propose a secure aggregation framework involving multiple servers based on coding theory, which is not only robust to clients dropping out and tolerant to partial server withdrawal but also resistant to malicious computation by servers and forgery attacks by adversaries. In particular, the proposed protocol employs the Chinese Residual Theorem (CRT) to encode private data and constructs Lagrange interpolation polynomials to perform aggregation, which achieves lightweight privacy preservation while achieving robust, verifiable and secure aggregation goals.