Privacy preservation based on clustering perturbation algorithm for social network

Yu, Fahong; Chen, Meijia; Yu, Bo; Ma, Longhua; Gao, Huimin

doi:10.1007/s11042-017-5502-3

Cited by 20 publications

(12 citation statements)

References 20 publications

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“…Then they replace the subgraph, which contains similar nodes into a cluster, by a super node. Also, in [30], Yu et al propose a clustering algorithm to preserve privacy for social network. The proposed algorithm ensures the privacy of nodes attributes values and community structures simultaneously.…”

Section: Related Workmentioning

confidence: 99%

Privacy preservation for social networks sequential publishing

2020

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The proliferation of social networks allowed creating a big quantity of data about users and their relationships. Such data contain much private information. Therefore, anonymization is required before publishing the data for data mining purposes (scientific research, marketing, decision support, etc). Most of the anonymization works in social networks focus on publishing one instance while not considering the need for anonymizing sequential releases. However, many cases show that sequential releases may infer private information even though individual instances are anonymized. This paper studies the privacy issues of sequential releases and proposes a privacy preserving solution for this case. The proposed solution ensures three privacy requirements (users' privacy, groups' privacy and edges' privacy), and it considers the case where many users and groups may share the same profiles. Some experiments over some complex queries show that the utility of the released data is better preserved than other solutions, with regard to the privacy of users, groups and edges.

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Section: Related Workmentioning

confidence: 99%

Privacy preservation for social networks sequential publishing

2020

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“…The basic idea of -anonymity is to remove some features so that each item is not distinguishable among other items. For data protection, it protects data at the cost of the original data quality [9]. For location-based services (typical social applications), it realizes the privacy protection through blurring user's locations [7,8,21].…”

Section: Related Workmentioning

confidence: 99%

“…Based on the -anonymity, a clustering perturbation algorithm for privacy protection in social networks was proposed [9]. It considers preserving privacy of vertex properties and community structures simultaneously.…”

Section: Related Workmentioning

confidence: 99%

“…Existing privacy protection schemes for social applications either introduce untrusted third parties [4][5][6] or sacrifice information accuracy [7][8][9][10][11]. In the schemes of introducing a third party, the third party acts as the agent of the users to submit the query requests to the server and returns the query results from the server to the users.…”

Section: Introductionmentioning

confidence: 99%

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Privacy Preservation for Friend-Recommendation Applications

Wang

Huang

2018

Security and Communication Networks

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Friend-recommendation applications as one kind of typical social applications can satisfy the social contact needs of different users and become tools for developing a social relationship. However, the privacy leakage has turned into an insurmountable obstacle to the market success of such applications. Existing privacy protection approaches for social applications either introduce untrusted third parties or sacrifice information accuracy. As for friend-recommendation applications particularly, the multihop trust chain and anonymous message methods still have a defect that the hacker can act as a user to acquire information. In this paper, we put forward the privacy protection mechanism based on zero knowledge without any privacy leakage to the application server. In detail, the server knows nothing about the user’s information, but can still provide users with accurate information on friend recommendation. We also analyze the potential attack methods and propose the corresponding solution. Our simulation results verify the effectivity and efficiency of our scheme.

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“…Various privacy protection methods have been proposed aiming at protecting these two types of privacy objects. For example, regarding the privacy of vertex attributes, data generalization [1][2][3][4] or perturbation methods [5][6][7] are usually adopted to protect personal identity or sensitive attributes such as name, phone number, address, etc from disclosure. On the other hand, for the privacy of the relational data [8], it has already become one of the research hotspots that needs to be explored more intensively.…”

Section: Introductionmentioning

confidence: 99%

Privacy Protection Method for Sensitive Weighted Edges in Social Networks

Gong¹,

Jin²,

Li³

et al. 2021

KSII TIIS

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Privacy vulnerability of social networks is one of the major concerns for social science research and business analysis. Most existing studies which mainly focus on un-weighted network graph, have designed various privacy models similar to k-anonymity to prevent data disclosure of vertex attributes or relationships, but they may be suffered from serious problems of huge information loss and significant modification of key properties of the network structure. Furthermore, there still lacks further considerations of privacy protection for important sensitive edges in weighted social networks. To address this problem, this paper proposes a privacy preserving method to protect sensitive weighted edges. Firstly, the sensitive edges are differentiated from weighted edges according to the edge betweenness centrality, which evaluates the importance of entities in social network. Then, the perturbation operations are used to preserve the privacy of weighted social network by adding some pseudo-edges or modifying specific edge weights, so that the bottleneck problem of information flow can be well resolved in key area of the social network. Experimental results show that the proposed method can not only effectively preserve the sensitive edges with lower computation cost, but also maintain the stability of the network structures. Further, the capability of defending against malicious attacks to important sensitive edges has been greatly improved.

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Privacy preservation based on clustering perturbation algorithm for social network

Cited by 20 publications

References 20 publications

Privacy preservation for social networks sequential publishing

Privacy preservation for social networks sequential publishing

Privacy Preservation for Friend-Recommendation Applications

Privacy Protection Method for Sensitive Weighted Edges in Social Networks

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