CenLocShare: A centralized privacy-preserving location-sharing system for mobile online social networks

Xiao, Xi; Chen, Chuansheng; Sangaiah, Arun Kumar; Hu, Guangwu; Ye, Runguo; Jiang, Yong

doi:10.1016/j.future.2017.01.035

Cited by 42 publications

(22 citation statements)

References 25 publications

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“…In [48] authors proposed a mechanism called CenLoc-Share to address the above mentioned issues. Firstly, to overcome the issues caused by having two different servers was solved by combining the Social Network Server (SNS), and Location Based Server (LBS), into a single server called Location Storing Social Network Server (LSSNS).…”

Section: A Location Privacymentioning

confidence: 99%

A Survey on Privacy Issues in Mobile Social Networks

Sai

2020

IEEE Access

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Mobile Social Networks (MSNs) are a category of social networks that have features of both traditional OSNs and Location-Based Social Networks (LBSNs). MSNs provide users with an opportunity to explore social media, connect with people nearby, or with those sharing similar interests. The ease with which people can connect and use these networks to obtain information, share user-centric data, and request services on the go through their mobile devices as long as they have access to the internet has made MSNs omnipresent. Due to the widespread nature of MSNs and the lack of effective privacy-preserving architecture makes them a target for several attacks by adversaries and the users of the networks are completely vulnerable to these privacy breaches. With the advancements in technology, the number of attacks and their possible solutions is complex and expansive. Therefore, there is a need to properly categorize them to understand the intricacies of different attacks and the concepts used by researchers to devise different solutions. This survey on privacy issues in MSNs serves as a way to provide an introduction to MSNs. We categorize privacy in MSN on the basis of the aspect they effect and summarize the different threats in each category. Furthermore, we provide an elaborate classification of the various privacy-preserving solutions proposed in recent research works, under each category of privacy. We also review different datasets and data generation tools that can be used by future researchers in this field.

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Section: A Location Privacymentioning

confidence: 99%

A Survey on Privacy Issues in Mobile Social Networks

Sai

2020

IEEE Access

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“…Also, they incur a high transmission and storage cost [28], [32], [33], [29], [30]. To address this issue, very recently, Xiao et al proposed CenLocShare [34], where SNS and LBS were amalgamated into one single server. This scheme reduces communication cost, storage cost and also increases user's privacy protection.…”

Section: Related Workmentioning

confidence: 99%

A Privacy-Preserving Efficient Location-Sharing Scheme for Mobile Online Social Network Applications

Bhattacharya

Roy²,

Mistry

et al. 2020

IEEE Access

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“…In the defined graph, when every node has k-1 edges. The degree of K-anonymity may oppose the opponent with the background node degree knowledge [10]. As shown in Figure 2, the data collection is defined by degree d = {2, 2, 2, 2, 1} in unique graph of social networking (a) and the degree collection is d={2,2,2,2,2} in two-anonymity social network graph (b) with the addition of one edge among 2 and 5 node.…”

Section: K-anonymity In Social Networkingmentioning

confidence: 99%

“…Simple anonymity is considered as simplest technology of anonymity, in which the node information for identification is restored by insignificant symbols like 1, 2...3 etc. Figure 2 below defines simple anonymity publishing in social networking [10]. This research has utilized k-anonymity privacy protection model that helps to protect the data.…”

Section: Introductionmentioning

confidence: 99%

Enhancement of k-anonymity algorithm for privacy preservation in social media

Sharma

Pathak

2018

IJET

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In recent times, more and more social data is transmitted in different ways. Protecting the privacy of social network data has turn out to be an essential issue. Hypothetically, it is assumed that the attacker utilizes the similar information used by the genuine user. With the knowledge obtained from the users of social networks, attackers can easily attack the privacy of several victims. Thus, assuming the attacks or noise node with the similar environment information does not resemble the personalized privacy necessities, meanwhile, it loses the possibility to attain better utility by taking benefit of differences of users’ privacy necessities. The traditional research on privacy-protected data publishing can only deal with relational data and even cannot applied to the data of social networking. In this research work, K-anonymity is used for providing the security of the sensitive information from the attacker in the social network. K-anonymity provides security from attacker by making the graph and developing nodes degree. The clusters are made by grouping the similar degree into one group and the process is repeated until the noisy node is identified. For measuring the efficiency the parameters named as Average Path Length (APL) and information loss are measured. A reduction of 0.43% of the information loss is obtained.

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CenLocShare: A centralized privacy-preserving location-sharing system for mobile online social networks

Cited by 42 publications

References 25 publications

A Survey on Privacy Issues in Mobile Social Networks

A Survey on Privacy Issues in Mobile Social Networks

A Privacy-Preserving Efficient Location-Sharing Scheme for Mobile Online Social Network Applications

Enhancement of k-anonymity algorithm for privacy preservation in social media

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