Enhancing social network privacy with accumulated non-zero prior knowledge

Wang, Yue; Yang, Lin; Chen, Xiaoyun; Zhang, Xiaofeng; He, Zhenyu

doi:10.1016/j.ins.2018.02.031

Cited by 10 publications

(3 citation statements)

References 13 publications

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“…In the experiments, seven networks without node attributes are first generated by using the LFR benchmark, of which parameters are given in Table 2. For the first four networks, four types of attributes are generated by using the LFR-EA, where the attribute ranges are set as [1,5], [1,10], [1,12] and [1,10], and the fluctuating parameter v are set as 0.2, 0.5, 0.2, and 0.5 separately. For the rest two networks, the iris [45] and breast cancer datasets [46] are employed to generate their node attributes, where each record owns four numerical attributes and with classification results.…”

Section: Compared Algorithms and Data Sets 511 Compared Algorithmsmentioning

confidence: 99%

“…However, owing to privacy concerns, many users are unwilling to provide their personal information to build social networks. To alleviate the privacy concern of users, many privacy-preserving methods have been developed to prevent the attacker from recognizing a specific user from a social network, such as disturbing the edges in social networks, restricting queries in social networks, and so on [3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

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A Privacy Preservation Method for Attributed Social Network Based on Negative Representation of Information

Jiang,

Liao,

Zhao

et al. 2024

CMES

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Due to the presence of a large amount of personal sensitive information in social networks, privacy preservation issues in social networks have attracted the attention of many scholars. Inspired by the self-nonself discrimination paradigm in the biological immune system, the negative representation of information indicates features such as simplicity and efficiency, which is very suitable for preserving social network privacy. Therefore, we suggest a method to preserve the topology privacy and node attribute privacy of attribute social networks, called AttNetNRI. Specifically, a negative survey-based method is developed to disturb the relationship between nodes in the social network so that the topology structure can be kept private. Moreover, a negative database-based method is proposed to hide node attributes, so that the privacy of node attributes can be preserved while supporting the similarity estimation between different node attributes, which is crucial to the analysis of social networks. To evaluate the performance of the AttNetNRI, empirical studies have been conducted on various attribute social networks and compared with several state-of-the-art methods tailored to preserve the privacy of social networks. The experimental results show the superiority of the developed method in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topology disturbing and attribute hiding parts. The experimental results show the superiority of the developed methods in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topological interference and attribute-hiding components.

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Section: Compared Algorithms and Data Sets 511 Compared Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Privacy Preservation Method for Attributed Social Network Based on Negative Representation of Information

Jiang,

Liao,

Zhao

et al. 2024

CMES

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show abstract

“…They showed that there was an inherent trade-off between privacy and accuracy when answering a large number of queries. Wang et al [59] formulated the correlated query results as the non-zero prior knowledge and proposed a novel differential privacy approach to enhance privacy of social network data being inferred. The proposed approach was superior to the state-of-the-art privacy-preserving approaches with respect to data privacy and data utility; however, this approach might be not extended to other types of social graph queries.…”

Section: Related Workmentioning

confidence: 99%

Privacy-Preserving Monotonicity of Differential Privacy Mechanisms

Liu

Zhou

et al. 2018

Applied Sciences

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Differential privacy mechanisms can offer a trade-off between privacy and utility by using privacy metrics and utility metrics. The trade-off of differential privacy shows that one thing increases and another decreases in terms of privacy metrics and utility metrics. However, there is no unified trade-off measurement of differential privacy mechanisms. To this end, we proposed the definition of privacy-preserving monotonicity of differential privacy, which measured the trade-off between privacy and utility. First, to formulate the trade-off, we presented the definition of privacy-preserving monotonicity based on computational indistinguishability. Second, building on privacy metrics of the expected estimation error and entropy, we theoretically and numerically showed privacy-preserving monotonicity of Laplace mechanism, Gaussian mechanism, exponential mechanism, and randomized response mechanism. In addition, we also theoretically and numerically analyzed the utility monotonicity of these several differential privacy mechanisms based on utility metrics of modulus of characteristic function and variant of normalized entropy. Third, according to the privacy-preserving monotonicity of differential privacy, we presented a method to seek trade-off under a semi-honest model and analyzed a unilateral trade-off under a rational model. Therefore, privacy-preserving monotonicity can be used as a criterion to evaluate the trade-off between privacy and utility in differential privacy mechanisms under the semi-honest model. However, privacy-preserving monotonicity results in a unilateral trade-off of the rational model, which can lead to severe consequences.

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