With the rapid development of Artificial Intelligence, fake data are being generated and circulated on an unprecedented scale. While such data may be entertaining in some contexts, data collectors are generally reluctant to accept them due to concerns about service quality and security risks. A central challenge, therefore, is how to incentivise contributors to provide authentic data. Existing research has mainly focused on regulating the use of fake data, with limited attention to designing mechanisms that motivate contributors to share real data. In this paper, we propose a pricing strategy with an embedded incentive mechanism that ensures contributors obtain higher financial benefits from sharing real data rather than fake data. We formulate the problem as a Stackelberg game, where the data collector acts as the leader and contributors are the followers. To enhance reliability, we integrate a peer-review method to help verify data authenticity, which in turn informs the incentive-compatible pricing design. We establish the existence and uniqueness of the equilibrium solution, and further conduct numerical simulations to demonstrate the equilibrium-seeking process and the influence of key parameters.

Federated learning (FL) requires clients to train constituted models based on their local datasets. Clients usually directly train local models using their entire datasets without distinguishing which information of data is task-relevant or irrelevant. Task-irrelevant information does not contribute to the learning task but exposes additional privacy information to adversaries. Studies have shown that unintended information leakage from gradients during FL iterations threatens clients' privacy. Researchers applied differential privacy (DP) to protect clients' gradients, but it does not help to reduce task-irrelevant information from the gradients. In this paper, we propose a compressive privacy federated learning (CP-FL) scheme to protect the task-irrelevant information from gradient leakage attacks. In CP-FL, clients train a local compressive model according to the global task. The local compressive model constructs a new representation, which extracts task-relevant and removes task-irrelevant information from clients' data. Since the global model is updated based on the compressed representation that eliminates the task-irrelevant information, it can effectively prevent adversaries from inferring those property values from the uploaded gradients. Moreover, with the help of a powerful local compressive model that sanitizes the challenging data into a low-dimension space representation, CP-FL can use a small global model instead of a sizeable one, significantly reducing communication. Both theoretical analysis and extensive experimental results demonstrate that CP-FL can effectively defend against gradient leakage attacks while maintaining practical utility.

Federated learning (FL) has become a hot research domain due to its privacy protection for model collaboratively training in edge computing systems. However, recent studies indicated that most FL algorithms have desperately suffered from backdoor attacks. Although many backdoor defence FL algorithms were proposed, their effects were highly related to the ratio of malicious clients (RMC) of all participated edge nodes. To be more specific, most of them only set RMC around 10% to 30% in their experiments, and their results also showed that the rate of successful backdoor defence seriously drops when RMC increases. In the paper, we propose a novel federated learning scheme with mode connectivity (FedMC) to defend against backdoor attacks, mitigating the sharp defence effect degradation as RMC increases. Conventional mode connectivity mainly focuses on training a connecting curve between two end models, which is inapplicable in distributed multiple clients FL situations. We extend the two-ends mode connectivity to multi-ends by introducing a scalable regularization term consisting of the edge clients' models to involve their knowledge in the connective model training. In each communication round, the FL-Server aggregates and absorbs the contribution of clients by training a connective model based on a small set of clean samples, which builds a pathway to accurately connect all edge clients' models and mitigates the backdoor triggers of models. Extensive experiments and results demonstrate that FedMC can effectively defend against backdoor attacks while maintaining the accuracy on untampered test data.

Machine unlearning has quickly emerged as a technique to withdraw users’ data from the trained model to protect their privacy. Yet the cost of completely unlearning by retraining is high and the unlearning service is thus hard to proceed in the market. We work on the SISA method that greatly lowers the cost of unlearning as an example in this manuscript. We model the problem with a game theory model that balances customers’ benefit and the service provider’s profit, and the optimal price is acquired that both parties could accept. More specifically, in the game model, we calculate customers’ average waiting time with bulk service queueing model, and linearly estimate the customers’ benefit in terms of privacy from withdrawing their data. Our method RUE shows that both parties get more benefit or profit than others, which could make the unlearning service run smoothly when the concerns about the high price are removed. We also analyse the influence of the waiting time on the number of unlearning requests and on the price.