A Study On Privacy-Preserving Mechanisms For Data Sharing

Data sharing serves as an effective means of achieving data integration and configuration.It enables the provision of essential data for tasks such as machine learning,thereby facilitating the training of high-quality models.However,a significant impediment to data sharing arises from the presence of sensitive user information within the shared data.Consequently,privacy concerns become a paramount factor that restricts the widespread adoption of data sharing practices.Therefore,in order to protect user privacy in data sharing,this paper proposes a data sharing mechanism based on distributed generative adversarial networks that provides personalized differential privacy guarantees for participants.Moreover,since users with different privacy preferences incur different privacy costs when participating in data sharing,this paper also proposes an incentive mechanism to encourage data owners to participate in data sharing over the long term.The main research topics covered in this paper are as follows:(1)Privacy-Preserving Data Sharing Framework: This framework utilizes an asynchronous distributed generative adversarial network to reconstruct datasets for downstream tasks by learning the joint data distribution of distributed users.The aim is to achieve privacy-preserving data sharing.(2)Personalized Differential Privacy Guarantee Method: Based on the data sharing framework,this method aims to address the privacy concerns faced by users during the assisted training process.It takes into account that different users have distinct privacy preferences and leverages the ? zero-concentrated differential privacy theory(?-z CDP)to offer personalized differential privacy guarantees to users.(3)Contract-Based Incentive Mechanism: This mechanism is designed from the perspective of privacy protection,with users’ privacy preferences as the main factor to incentivize them to contribute data.Additionally,the mechanism transforms the contract design problem into a constrained optimization problem to achieve the optimal contract solution.In addition,to verify the feasibility and effectiveness of our proposed solution,we perform privacy analysis and experimental testing.Our results demonstrate that the solution is both feasible and efficient.It delivers personalized privacy guarantees for participants while facilitating data sharing.In comparison to other data generation methods based on generative adversarial networks with differential privacy guarantees,our proposed scheme generates higher quality data at the same privacy cost and better enables downstream tasks to be completed.