PCP: A Privacy-Preserving Content-Based Publish–Subscribe Scheme With Differential Privacy in Fog Computing

Abstract

Fog computing dramatically extends the cloud computing to the edge of the network and admirably solves the problem that the brokers (in publish-subscribe system) generally lack of computing capacity and energy power. However, brokers may be disguised, hacked, sniffed, and corrupted. The traditional security technology cannot protect the system privacy when facing a possible collusion attack. In this paper, we propose a privacy-preserving content-based publish/subscribe scheme with differential privacy in fog computing context, named PCP, where the fog nodes act as the brokers. Specifically, PCP firstly utilizes the U-Apriori algorithm to mine the top-K frequent itemsets (i.e., the attributes) from uncertain data sets, then applies the exponential and Laplace mechanism to ensure the differential privacy, and the broker uses the mined top-K itemsets to match appropriate publisher and subscriber finally. Security analysis shows that the PCP can guarantee differential privacy in theory. To evaluate the performance of PCP, we carry out experiments with real-world scenario data sets. The experimental results show that PCP efficiently achieves the tradeoff between the system cost and the privacy demand.

Keywords

• Publish–subscribe
• differential privacy
• fog computing
• privacy-preserving
• uncertain datasets