Utility-Oriented K-Anonymization on Social Networks

Wang, Yazhe; Xie, Long; Zheng, Baihua; Lee, Ken C. K.

doi:10.1007/978-3-642-20149-3_8

Cited by 11 publications

(9 citation statements)

References 10 publications

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“…The main idea of the node K-anonymity [2][3][4][5][6] and subgraph K-anonymity [7][8][9][10][11][12][13]: there are at least k candidates in the anonymized social network while attackers identify a special object based on background knowledge, that is to say, the probability of privacy disclosure is less than 1/K.…”

Section: Privacy Preserving Technology In Social Networkmentioning

confidence: 99%

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Privacy Protection in Mobile Social Network in the Context of Big Data

Wang¹,

Wang²

2017

Proceedings of the 2017 International Conference on Management, Education and Social Science (ICMESS 2017)

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Abstract-With the rapid development of Web 2.0 technology, mobile social network data has shown the classical big data characteristics. The analysis of social network big data is getting deeper and deeper. At the same time, the risk of privacy disclosure in social network is also very obvious. A series of privacy preserving models and algorithms for social network data are proposed. In this paper, we summarized the privacy leakage type of social network, such as attacks on social network nodes and edges, deeply analyzed the existing privacy preserving technology from the following aspects: node K-anonymity, sub-graph K-anonymity, and data disturbance, pointed out its advantages and disadvantages, and prospected the future research directions from four aspects.

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Section: Privacy Preserving Technology In Social Networkmentioning

confidence: 99%

“…In literature [9], the loss of community structure information is minimized while the K-degree anonymous graph is generated.…”

Section: Subgraph K-anonymitymentioning

confidence: 99%

Privacy Protection in Mobile Social Network in the Context of Big Data

Wang¹,

Wang²

2017

Proceedings of the 2017 International Conference on Management, Education and Social Science (ICMESS 2017)

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“…Additionally, the k-anonymity l-diversity models aim to sanitize the published graph, eventually leading to data usability reduction for published social network data. Therefore, the tradeoff between the individual's privacy security and data utility while publishing the social network data has become a major concern [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

“…Although researchers have proposed various anonymous models based on k-anonymity [6] to achieve privacy protection in existing research [7][8][9], the balance between privacy safety and data utility is still new in the field of social network publishing [4]. The existing approaches may prevent leakage of some privacy information when publishing social network data, but may result in nontrivial utility loss without exploring the attribute of sparse distribution and without recognizing the fact that different nodes have different impacts on the network structure.…”

Section: Introductionmentioning

confidence: 99%

A Differentiated Anonymity Algorithm for Social Network Privacy Preservation

Xie

Zheng

2016

Algorithms

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Devising methods to publish social network data in a form that affords utility without compromising privacy remains a longstanding challenge, while many existing methods based on k-anonymity algorithms on social networks may result in nontrivial utility loss without analyzing the social network topological structure and without considering the attributes of sparse distribution. Toward this objective, we explore the impact of the attributes of sparse distribution on data utility. Firstly, we propose a new utility metric that emphasizes network structure distortion and attribute value loss. Furthermore, we design and implement a differentiated k-anonymity l-diversity social network anonymity algorithm, which seeks to protect users' privacy in social networks and increase the usability of the published anonymized data. Its key idea is that it divides a node into two child nodes and only anonymizes sensitive values to satisfy anonymity requirements. The evaluation results show that our method can effectively improve the data utility as compared to generalized anonymizing algorithms.

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“…In this paper, we summarized the privacy leakage type of social network, deeply analyzed the existing privacy preserving technologies [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] in social network, pointed out their advantages and disadvantages, and prospected the future research directions.…”

Section: Introductionmentioning

confidence: 99%

Privacy Protection in Online Social Network in the Context of Big Data

Wang¹,

Wang²,

Chen³

2016

Proceedings of the 2015 3rd International Conference on Education, Management, Arts, Economics and Social Science

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With the rapid development of Web 2.0 technology, online social network data has shown the classical big data characteristics. The analysis of social network big data is getting deeper and deeper. At the same time, the risk of privacy disclosure in social network is also very obvious. A series of privacy preserving models and algorithms for social network data are proposed. In this paper, we summarized the privacy leakage type of social network, deeply analyzed the existing privacy preserving technology, pointed out its advantages and disadvantages, and prospected the future research directions.

show abstract

Utility-Oriented K-Anonymization on Social Networks

Cited by 11 publications

References 10 publications

Privacy Protection in Mobile Social Network in the Context of Big Data

Privacy Protection in Mobile Social Network in the Context of Big Data

A Differentiated Anonymity Algorithm for Social Network Privacy Preservation

Privacy Protection in Online Social Network in the Context of Big Data

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