Federated learning (FL) has emerged as an ideal privacy-preserving learning technique which can train a global model in a collaborative way while preserving the private data in the local. However, recent advances have demonstrated that FL is still vulnerable to inference attacks, such as reconstruction attack and membership inference. Among these attacks, the property inference attack, aiming to infer properties of the training data that are irrelevant with the learning objective, has not received too much attention while resulting in severe privacy leakage. Existing property inference attack approaches either cannot achieve satisfactory performance when the global model has converged or under dynamic FL where participants can drop in and drop out freely. In this paper, we propose a novel poisoning-assisted property inference attack (PAPI-attack) against FL. The key insight is that there exists underlying discriminative ability in the periodic model updates, which reflects the change of the data distribution, especially the occurrence of the sensitive property. Thus, a binary attack model can be constructed by a malicious participant for inferring the unintended information. More importantly, we present a property-specific poisoning mechanism by modifying the label of training data from the adversary to distort the decision boundary of shared (global) model in FL. Consequently, benign participants are induced to disclose more information about the sensitive property. Extensive experiments on real-world datasets demonstrate that PAPI-attack outperforms the state-of-the-art property inference attacks against FL.

Worker recruitment is an important problem in mobile crowdsourcing (MCS), which aims to find sufficient and suitable participants to perform tasks. However, existing worker recruitment approaches mainly focus on how to select the most suitable workers for tasks from a large worker pool, while the recruitment problem under insufficient workers (e.g., a new MCS system) has not been well addressed. In this paper, we focus on the insufficient participation problem of MCS systems with limited number of workers, and propose to leverage social network to recruit workers for task completion as well as expanding the worker pool. To this end, we propose a dynamic incentive mechanism, called SocialRecruiter, to encourage workers on the MCS platform to propagate tasks through social networks, so that inviting friends to join in the MCS platform to further propagate and complete tasks. Motivated by the SIR epidemic model, we propose a novel task-specific epidemic model to characterize the status change of users for task propagation and completion through social networks. In order to encourage task completion and propagation, the propagating reward and completing reward are provided according to workers’ actions. In particular, in order to maximize the task completion within the financial budget, the propagating and completing rewards are dynamically updated at each cycle according to real-time worker recruitment progress. The extensive experimental results on two real-world datasets demonstrate that SocialRecruiter outperforms the state-of-the-art approaches in terms of worker recruitment and task completion.