With the rapid development of e-commerce, the security issues of collaborative filtering recommender systems have been widely investigated. Malicious users can benefit from injecting a great quantities of fake profile...With the rapid development of e-commerce, the security issues of collaborative filtering recommender systems have been widely investigated. Malicious users can benefit from injecting a great quantities of fake profiles into recommender systems to manipulate recommendation results. As one of the most important attack methods in recommender systems, the shilling attack has been paid considerable attention, especially to its model and the way to detect it. Among them, the loose version of Group Shilling Attack Generation Algorithm (GSAGenl) has outstanding performance. It can be immune to some PCC (Pearson Correlation Coefficient)-based detectors due to the nature of anti-Pearson correlation. In order to overcome the vulnerabilities caused by GSAGenl, a gravitation-based detection model (GBDM) is presented, integrated with a sophisticated gravitational detector and a decider. And meanwhile two new basic attributes and a particle filter algorithm are used for tracking prediction. And then, whether an attack occurs can be judged according to the law of universal gravitation in decision-making. The detection performances of GBDM, HHT-SVM, UnRAP, AP-UnRAP Semi-SAD,SVM-TIA and PCA-P are compared and evaluated. And simulation results show the effectiveness and availability of GBDM.展开更多
Numerous privacy-preserving issues have emerged along with the fast development of the Internet of Things. In addressing privacy protection problems in Wireless Sensor Networks （WSN）, secure multi-party computation ...Numerous privacy-preserving issues have emerged along with the fast development of the Internet of Things. In addressing privacy protection problems in Wireless Sensor Networks （WSN）, secure multi-party computation is considered vital, where obtaining the Euclidian distance between two nodes with no disclosure of either side＇s secrets has become the focus of location-privacy-related applications. This paper proposes a novel Privacy-Preserving Scalar Product Protocol （PPSPP） for wireless sensor networks. Based on PPSPP, we then propose a Homomorphic-Encryption-based Euclidean Distance Protocol （HEEDP） without third parties. This protocol can achieve secure distance computation between two sensor nodes. Correctness proofs of PPSPP and HEEDP are provided, followed by security validation and analysis. Performance evaluations via comparisons among similar protocols demonstrate that HEEDP is superior; it is most efficient in terms of both communication and computation on a wide range of data types, especially in wireless sensor networks.展开更多
基金supported by the National Natural Science Foundation of P.R.China(No.61672297)the Key Research and Development Program of Jiangsu Province(Social Development Program,No.BE2017742)+1 种基金The Sixth Talent Peaks Project of Jiangsu Province(No.DZXX-017)Jiangsu Natural Science Foundation for Excellent Young Scholar(No.BK20160089)
文摘With the rapid development of e-commerce, the security issues of collaborative filtering recommender systems have been widely investigated. Malicious users can benefit from injecting a great quantities of fake profiles into recommender systems to manipulate recommendation results. As one of the most important attack methods in recommender systems, the shilling attack has been paid considerable attention, especially to its model and the way to detect it. Among them, the loose version of Group Shilling Attack Generation Algorithm (GSAGenl) has outstanding performance. It can be immune to some PCC (Pearson Correlation Coefficient)-based detectors due to the nature of anti-Pearson correlation. In order to overcome the vulnerabilities caused by GSAGenl, a gravitation-based detection model (GBDM) is presented, integrated with a sophisticated gravitational detector and a decider. And meanwhile two new basic attributes and a particle filter algorithm are used for tracking prediction. And then, whether an attack occurs can be judged according to the law of universal gravitation in decision-making. The detection performances of GBDM, HHT-SVM, UnRAP, AP-UnRAP Semi-SAD,SVM-TIA and PCA-P are compared and evaluated. And simulation results show the effectiveness and availability of GBDM.
基金sponsored by the National Natural Science Foundation of China(No.61373138)the Natural Science Key Fund for Colleges and Universities in Jiangsu Province(No.12KJA520002)+4 种基金the Key Research and Development Program of Jiangsu Province(Social Development Program)(No.BE2015702)the Postdoctoral Foundation(Nos.2015M570468 and2016T90485)the Sixth Talent Peaks Project of Jiangsu Province(No.DZXX-017)the Fund of Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks(WSNLBZY201516)the Science and Technology Innovation Fund for Postgraduate Education of Jiangsu Province(No.KYLX15 0853)
文摘Numerous privacy-preserving issues have emerged along with the fast development of the Internet of Things. In addressing privacy protection problems in Wireless Sensor Networks （WSN）, secure multi-party computation is considered vital, where obtaining the Euclidian distance between two nodes with no disclosure of either side＇s secrets has become the focus of location-privacy-related applications. This paper proposes a novel Privacy-Preserving Scalar Product Protocol （PPSPP） for wireless sensor networks. Based on PPSPP, we then propose a Homomorphic-Encryption-based Euclidean Distance Protocol （HEEDP） without third parties. This protocol can achieve secure distance computation between two sensor nodes. Correctness proofs of PPSPP and HEEDP are provided, followed by security validation and analysis. Performance evaluations via comparisons among similar protocols demonstrate that HEEDP is superior; it is most efficient in terms of both communication and computation on a wide range of data types, especially in wireless sensor networks.
