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Comput Commun ; 162: 139-151, 2020 Oct 01.
Artigo em Inglês | MEDLINE | ID: mdl-32873996


Frequency estimation of physical symptoms for peoples is the most direct way to analyze and predict infectious diseases. In Internet of medical Things (IoMT), it is efficient and convenient for users to report their physical symptoms to hospitals or disease prevention departments by various mobile devices. Unfortunately, it usually brings leakage risk of these symptoms since data receivers may be untrusted. As a strong metric for health privacy, local differential privacy (LDP) requires that users should perturb their symptoms to prevent the risk. However, the widely-used data structure called sketch for frequency estimation does not satisfy the specified requirement. In this paper, we firstly define the problem of frequency estimation of physical symptoms under LDP. Then, we propose four different protocols, i.e., CMS-LDP, FCS-LDP, CS-LDP and FAS-LDP to solve the above problem. Next, we demonstrate that the designed protocols satisfy LDP and unbiased estimation. We also present two approaches to implement the key component (i.e., universal hash functions) of protocols. Finally, we conduct experiments to evaluate four protocols on two real-world datasets, representing two different distributions of physical symptoms. The results show that CMS-LDP and CS-LDP have relatively optimal utility for frequency estimation of physical symptoms in IoMT.

IEEE J Biomed Health Inform ; 24(10): 3002-3011, 2020 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-32086224


Non-negative Matrix Factorization (NMF) is a dimensionality reduction approach for learning a parts-based and linear representation of non-negative data. It has attracted more attention because of that. In practice, NMF not only neglects the manifold structure of data samples, but also overlooks the priori label information of different classes. In this paper, a novel matrix decomposition method called Hyper-graph regularized Constrained Non-negative Matrix Factorization (HCNMF) is proposed for selecting differentially expressed genes and tumor sample classification. The advantage of hyper-graph learning is to capture local spatial information in high dimensional data. This method incorporates a hyper-graph regularization constraint to consider the higher order data sample relationships. The application of hyper-graph theory can effectively find pathogenic genes in cancer datasets. Besides, the label information is further incorporated in the objective function to improve the discriminative ability of the decomposition matrix. Supervised learning with label information greatly improves the classification effect. We also provide the iterative update rules and convergence proofs for the optimization problems of HCNMF. Experiments under The Cancer Genome Atlas (TCGA) datasets confirm the superiority of HCNMF algorithm compared with other representative algorithms through a set of evaluations.

Sensors (Basel) ; 18(9)2018 Sep 10.
Artigo em Inglês | MEDLINE | ID: mdl-30201933


With the development of the Internet of Things (IoT) technology, a vast amount of the IoT data is generated by mobile applications from mobile devices. Cloudlets provide a paradigm that allows the mobile applications and the generated IoT data to be offloaded from the mobile devices to the cloudlets for processing and storage through the access points (APs) in the Wireless Metropolitan Area Networks (WMANs). Since most of the IoT data is relevant to personal privacy, it is necessary to pay attention to data transmission security. However, it is still a challenge to realize the goal of optimizing the data transmission time, energy consumption and resource utilization with the privacy preservation considered for the cloudlet-enabled WMAN. In this paper, an IoT-oriented offloading method, named IOM, with privacy preservation is proposed to solve this problem. The task-offloading strategy with privacy preservation in WMANs is analyzed and modeled as a constrained multi-objective optimization problem. Then, the Dijkstra algorithm is employed to evaluate the shortest path between APs in WMANs, and the nondominated sorting differential evolution algorithm (NSDE) is adopted to optimize the proposed multi-objective problem. Finally, the experimental results demonstrate that the proposed method is both effective and efficient.

Sensors (Basel) ; 18(7)2018 Jun 26.
Artigo em Inglês | MEDLINE | ID: mdl-29949893


Service recommendation has become an effective way to quickly extract insightful information from massive data. However, in the cloud environment, the quality of service (QoS) data used to make recommendation decisions are often monitored by distributed sensors and stored in different cloud platforms. In this situation, integrating these distributed data (monitored by remote sensors) across different platforms while guaranteeing user privacy is an important but challenging task, for the successful service recommendation in the cloud environment. Locality-Sensitive Hashing (LSH) is a promising way to achieve the abovementioned data integration and privacy-preservation goals, while current LSH-based recommendation studies seldom consider the possible recommendation failures and hence reduce the robustness of recommender systems significantly. In view of this challenge, we develop a new LSH variant, named converse LSH, and then suggest an exception handling approach for recommendation failures based on the converse LSH technique. Finally, we conduct several simulated experiments based on the well-known dataset, i.e., Movielens to prove the effectiveness and efficiency of our approach.