DCAuth: Data-Centric Authentication for Secure In-Network Big-Data Retrieval

Abstract

Big data raises a strong demand on a network infrastructure to support the secure and efficient data retrieval with in-network caching. Information-Centric Networking (ICN) is an emerging approach to satisfy this demand, where big data are ubiquitously cached at the intermediate physical entities (IPEs) in the network and users retrieve the published data from the close copy holders. For the ICN, the unpredictability of users, IPEs, copy holders, and publishers during in-network big data retrievals poses a challenge to design a data-centric authentication mechanism to inhibit the malicious users to flood data requests and prevent the fake data from being cached and provided. However, the existing work only provides the authentications between users and publishers and suffers from the delay enlargement problem. To solve these problems, we design a trust model, namely a suspension-chain model (SCM), which is a trust chain that seamlessly merges certificate authority (CA)-based trust and neighbor-based trust. Based on SCM, we propose the DCAuth integrating certificate collection and packet forwarding, where the suspension certificate chain can be constructed for realizing any authentication to the unpredictable users/IPEs/publishers without accessing servers. Extensive simulations have been conducted to compare DCAuth with the existing work, which shows that delay can be greatly reduced and attacks can be efficiently prevented by DCAuth.