Distributed Differential Utility/Cost Analysis for Privacy Protection

In the era of Big Data, as vast amounts of data are collected and shared among collaborators or uploaded to the Internet, the attacks on data privacy become more and more serious. Compressive privacy (CP) is a kind of privacy-preserving dimension-reduced projection schemes such that the projected data can be well used for the intended utility task but not for malicious applications. Nevertheless, most of existing CP approaches belong to centralized processing, which are not applicable to the cases that data is dispersedly collected/stored at distributed nodes and cannot be centralized to one node for processing due to various reasons. To tackle this problem, we propose a distributed differential utility/cost analysis (dDUCA), in which each node in the network is only allowed to exchange and combine the compressive-and-lossy projection matrix with its one-hop neighbors. Using the projection matrix, the classification of the projected data is performed. Experiments on several datasets confirm the effectiveness of the proposed method in terms of both privacy protection and utility retention.