An Ownership Verification Mechanism Against Encrypted Forwarding Attacks in Data-Driven Social Computing

Abstract

Data-driven deep learning has accelerated the spread of social computing applications. To develop a reliable social application, service providers need massive data on human behavior and interactions. As the data is highly relevant to users’ privacy, researchers have conducted extensive research on how to securely build a collaborative training model. Cryptography methods are an essential component of collaborative training which is used to protect privacy information in gradients. However, the encrypted gradient is semantically invisible, so it is difficult to detect malicious participants forwarding other’s gradient to profit unfairly. In this paper, we propose a data ownership verification mechanism based on Σ-protocol and Pedersen commitment, which can help prevent gradient stealing behavior. We deploy the Paillier algorithm on the encoded gradient to protect privacy information in collaborative training. In addition, we design a united commitment scheme to complete the verification process of commitments in batches, and reduce verification consumption for aggregators in large-scale social computing. The evaluation of the experiments demonstrates the effectiveness and efficiency of our proposed mechanism.