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.
Highlights
Social computing, a field highly relevant to human behavior, has made considerable progress in recent years
We propose a data ownership verification mechanism to counter encrypted forwarding attacks caused by cryptography-based privacy-preserving methods
We propose an ownership verification mechanism based on the Patterson commitment that can prove that the user owns the plaintext of the data without providing the plaintext
Summary
A field highly relevant to human behavior, has made considerable progress in recent years. With the proliferation of cell phones and IoT devices, information about humans, behaviors, and interactions is being recorded in unprecedented detail. Combined with deep learning models, the application of social computing can profoundly solve various industry challenges such as epidemic prediction [1, 2], social network service [3], and hot topic recommendations [4]. As a data-driven technology, deep learning has a natural desire for data to be more accurate and higher-quality. Since user data in social computing is closely tied to privacy, providing the data directly would cause extremely serious privacy damage. Without directly obtaining local data from multiple parties, it can take full advantage of the value of the data by sharing gradients
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