CAE: Collusion Attack Emulator for Privacy-Preserving Data Aggregation Schemes

Abstract

In a number of networking applications, preserving the privacy of user-related data in data aggregation schemes is a fundamental issue. As a fact, many privacy-preserving protocols can be guaranteed with security against individual attacks, but they may be threatened by collusion between participants. Therefore, security analysis, especially for collusion attack analysis, plays an essential role in privacy-preserving data aggregation protocols. There do exist a few collusion attack schemes on data aggregation protocols, but none study the internal security mechanism of these protocols. In this paper, to our best knowledge, we are the first to propose a new kind of collusion attack analysis tool, which is named CAE (Collusion Attack Emulator). We employ it to check and judge the security of several existing privacy-preserving data aggregation schemes. We first show that for an aggregation scheme which has been known to be vulnerable under collusion attack, we can use CAE to explain why it is insecure. Then we demonstrate the blind detection function of CAE, i.e., we do not know whether an aggregation protocol is secure beforehand, and employ CAE to check its security and (if the protocol cannot pass the CAE test and thus to be insecure) to find its loophole.