An Adaptive Authenticated Data Structure With Privacy Preserving for Big Data Stream in Cloud

Abstract:

With the rapid development of 5G network, big data and IoT, data in many environments is often continuously and dynamically generated with high growth rates, just like stream. Thus, we call it big data stream, which plays an increasingly important role in all walks of life. However, how to verify its authenticity becomes a challenge when this big data stream in an untrusted environment such as cloud platform, for it faces the problems just like delay-sensitive, unpredictable data size and privacy leaks caused by third-party audits. To solve these problems, we propose a new authenticate data structure named privacy-preserving adaptive trapdoor hash authentication tree (P-ATHAT) by introducing trapdoor hash and BLS signature to the Merkle hash tree. The P-ATHAT scheme realizes real-time verification of data stream and can dynamically expand its structure as the data stream arrives. These characteristics not only shorten the authentication path but also solve the single point failure problem of the conventional authentication trees and enhance the robustness of the scheme. Moreover, we construct a homomorphic verification scheme above tree structure to solve the privacy leakage problem in third-party audit. Finally, security analysis and detailed experimental evaluation are performed on the proposed scheme, both results demonstrate that it is desirable for big data stream authentication and privacy-preserving in practical application.