A Private and Efficient Mechanism for Data Uploading in Smart Cyber-Physical Systems

Cai, Zhipeng; Zheng, Xu

doi:10.1109/tnse.2018.2830307

Cited by 494 publications

(231 citation statements)

References 28 publications

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“…PINQ proposed an algebraic method to describe the protection of privacy data analysis, and they ensured that the results satisfied difference privacy. Differential privacy is also frequently studied on the context of index [38][39][40][41][42][43][44][45][46]. This paper combines matrix manipulation of attributes with differential privacy technology, and we propose a novel privacy protection method to prevent attackers to infer user's sensitive information.…”

Section: Privacy Protectmentioning

confidence: 99%

Differentially Private Recommendation System Based on Community Detection in Social Network Applications

Cai

Yin

et al. 2018

Security and Communication Networks

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The recommender system is mainly used in the e-commerce platform. With the development of the Internet, social networks and e-commerce networks have broken each other’s boundaries. Users also post information about their favorite movies or books on social networks. With the enhancement of people’s privacy awareness, the personal information of many users released publicly is limited. In the absence of items rating and knowing some user information, we propose a novel recommendation method. This method provides a list of recommendations for target attributes based on community detection and known user attributes and links. Considering the recommendation list and published user information that may be exploited by the attacker to infer other sensitive information of users and threaten users’ privacy, we propose the CDAI (Infer Attributes based on Community Detection) method, which finds a balance between utility and privacy and provides users with safer recommendations.

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Section: Privacy Protectmentioning

confidence: 99%

Differentially Private Recommendation System Based on Community Detection in Social Network Applications

Cai

Yin

et al. 2018

Security and Communication Networks

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“…Location-based social networks (LBSNs) are a kind of social networks which include geographical information into shared contents. Many research problems emerge in LBSNs such as information propagation [6][7][8][9], privacy, and security [10][11][12][13][14]. Among them, friends and POI recommendation aiming at helping users make friends and find new places are two important services in LBSNs that have attracted a lot of attentions recently [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Friend and POI recommendation based on social trust cluster in location-based social networks

Zhu

Wang

Guo

et al. 2019

J Wireless Com Network

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Friend and point-of-interest (POI) recommendation are two primary individual services in location-based social networks (LBSNs). Major social platforms such as Foursquare and Instagram are all capable of recommending friends or POIs to individuals. However, most of these social websites make recommendations only based on similarity, popularity, or geographical influence; social trust among individuals has not been considered in those recommendation system. Recently, trust relationship has been proved to be helpful in collaborative recommendation. In this paper, we first propose algorithm to identify trust clusters and then give a trust prediction method based on these trust clusters. Then we combine the trust value and similarity among individuals to recommend friends to the target user. As for the POI recommendation, we devise a hybrid framework that integrates user preference, geographical influence, and trust relationship to improve the recommendation quality. In order to validate the effectiveness and efficiency of our methods, a series of experiments on two real social networks Foursquare and Instagram are conducted. The experiment results show that the trust cluster-based recommendation approach outperforms the baseline recommendation approaches in precision and recall.

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“…Due to the explosiveness of big data, many new highperformance requirements of network throughput, real-time performance, security privacy, and bandwidth have been put forward [1][2][3][4]. It is consequently challenging to design efficient link scheduling algorithms to improve communication efficiency in wireless communication.…”

Section: Introductionmentioning

confidence: 99%

Distributed Link Scheduling Algorithm Based on Successive Interference Cancellation in MIMO Wireless Networks

Lin

et al. 2019

Wireless Communications and Mobile Computing

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The performance of multiple input multiple output (MIMO) wireless networks is limited mainly by concurrent interference among sensor nodes. Effective link scheduling algorithms with the technology of successive interference cancellation (SIC) can maximize throughput in MIMO wireless networks. Most previous works on link scheduling in MIMO wireless networks did not consider SIC. In this paper, we propose a MIMO-SIC (MSIC) algorithm under the SINR model. First, a mathematical framework is established for the cross-layer optimization of routing and scheduling, with constraints of traffic balance and link capacity. Second, the interference regions are divided to characterize the level of interference between links. Finally, we propose a distributed link scheduling algorithm based on MSIC to eliminate the interference between competing links in the MIMO network. Experimental results show that the MSIC algorithm can increase the end-to-end throughput per unit by approximately 73% on average compared with non-SIC algorithms.

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A Private and Efficient Mechanism for Data Uploading in Smart Cyber-Physical Systems

Cited by 494 publications

References 28 publications

Differentially Private Recommendation System Based on Community Detection in Social Network Applications

Differentially Private Recommendation System Based on Community Detection in Social Network Applications

Friend and POI recommendation based on social trust cluster in location-based social networks

Distributed Link Scheduling Algorithm Based on Successive Interference Cancellation in MIMO Wireless Networks

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