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The recent proliferation of human-carried mobile devices has given rise to the crowd sensing systems. However, the sensory data provided by individual participants are usually not reliable. To identify truthful values from the crowd sensing data, the topic of truth discovery, whose goal is to estimate user quality and infer truths through quality-aware data aggregation, has drawn significant attention. Though able to improve aggregation accuracy, existing truth discovery approaches fail to take into consideration an important issue in their design, i.e., the protection of individual users' private information. In this paper, we propose a novel cloud-enabled privacy-preserving truth discovery (PPTD) framework for crowd sensing systems, which can achieve the protection of not only users' sensory data but also their reliability scores derived by the truth discovery approaches. The key idea of the proposed framework is to perform weighted aggregation on users' encrypted data using homomorphic cryptosystem. In order to deal with large-scale data, we also propose to parallelize PPTD with MapReduce framework. Through extensive experiments on not only synthetic data but also real world crowd sensing systems, we justify the guarantee of strong privacy and high accuracy of our proposed framework.

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Knowledge graph embedding (KGE) is a technique for learning continuous embeddings for entities and relations in the knowledge graph. Due to its benefit to a variety of downstream tasks such as knowledge graph completion, question answering and recommendation, KGE has gained significant attention recently. Despite its effectiveness in a benign environment, KGE's robustness to adversarial attacks is not well-studied. Existing attack methods on graph data cannot be directly applied to attack the embeddings of knowledge graph due to its heterogeneity. To fill this gap, we propose a collection of data poisoning attack strategies, which can effectively manipulate the plausibility of arbitrary targeted facts in a knowledge graph by adding or deleting facts on the graph. The effectiveness and efficiency of the proposed attack strategies are verified by extensive evaluations on two widely-used benchmarks.

The recent proliferation of human-carried mobile devices has given rise to the crowd sensing systems. However, the sensory data provided by individual participants are usually not reliable. To better utilize such sensory data, the topic of truth discovery, whose goal is to estimate user quality and infer reliable aggregated results through quality-aware data aggregation, has drawn significant attention. Though able to improve aggregation accuracy, existing truth discovery approaches fail to address the privacy concerns of individual users. In this article, we propose a novel privacy-preserving truth discovery (PPTD) framework, which can protect not only users’ sensory data but also their reliability scores derived by the truth discovery approaches. The key idea of the proposed framework is to perform weighted aggregation on users’ encrypted data using a homomorphic cryptosystem, which can guarantee both high accuracy and strong privacy protection. In order to deal with large-scale data, we also propose to parallelize PPTD with MapReduce framework. Additionally, we design an incremental PPTD scheme for the scenarios where the sensory data are collected in a streaming manner. Extensive experiments based on two real-world crowd sensing systems demonstrate that the proposed framework can generate accurate aggregated results while protecting users’ private information.

Pairwise learning has received much attention recently as it is more capable of modeling the relative relationship between pairs of samples. Many machine learning tasks can be categorized as pairwise learning, such as AUC maximization and metric learning. Existing techniques for pairwise learning all fail to take into consideration a critical issue in their design, i.e., the protection of sensitive information in the training set. Models learned by such algorithms can implicitly memorize the details of sensitive information, which offers opportunity for malicious parties to infer it from the learned models. To address this challenging issue, in this paper, we propose several differentially private pairwise learning algorithms for both online and offline settings. Specifically, for the online setting, we first introduce a differentially private algorithm (called OnPairStrC) for strongly convex loss functions. Then, we extend this algorithm to general convex loss functions and give another differentially private algorithm (called OnPairC). For the offline setting, we also present two differentially private algorithms (called OffPairStrC and OffPairC) for strongly and general convex loss functions, respectively. These proposed algorithms can not only learn the model effectively from the data but also provide strong privacy protection guarantee for sensitive information in the training set. Extensive experiments on real-world datasets are conducted to evaluate the proposed algorithms and the experimental results support our theoretical analysis.

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