The (P, &amp;#x03B1;, K) anonymity model for privacy protection of personal information in the social networks

Kong, Qingjiang; Wang, Xiaohao; Zhang, Jun

doi:10.1109/itaic.2011.6030363

Cited by 3 publications

(1 citation statement)

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“…In [47], Xiao and Tao present a generalization framework based on the concept of personalized anonymity, where an individual can specify the degree of privacy protection for his sensitive data. Some variants of k-anonymity have also been proposed to support personalized privacy preservation, such as (P, α, K )-anonymity [48], personalized (α, k)-anonymity [49], PK -anonymity [50], individualized (α, k)-anonymity [51], etc. In current studies, individual's personalized preference on privacy preserving is formulated through the parameters of the anonymity model (e.g.…”

Section: Discussionmentioning

confidence: 99%

Information Security in Big Data: Privacy and Data Mining

et al. 2014

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The growing popularity and development of data mining technologies bring serious threat to the security of individual's sensitive information. An emerging research topic in data mining, known as privacypreserving data mining (PPDM), has been extensively studied in recent years. The basic idea of PPDM is to modify the data in such a way so as to perform data mining algorithms effectively without compromising the security of sensitive information contained in the data. Current studies of PPDM mainly focus on how to reduce the privacy risk brought by data mining operations, while in fact, unwanted disclosure of sensitive information may also happen in the process of data collecting, data publishing, and information (i.e., the data mining results) delivering. In this paper, we view the privacy issues related to data mining from a wider perspective and investigate various approaches that can help to protect sensitive information. In particular, we identify four different types of users involved in data mining applications, namely, data provider, data collector, data miner, and decision maker. For each type of user, we discuss his privacy concerns and the methods that can be adopted to protect sensitive information. We briefly introduce the basics of related research topics, review state-of-the-art approaches, and present some preliminary thoughts on future research directions. Besides exploring the privacy-preserving approaches for each type of user, we also review the game theoretical approaches, which are proposed for analyzing the interactions among different users in a data mining scenario, each of whom has his own valuation on the sensitive information. By differentiating the responsibilities of different users with respect to security of sensitive information, we would like to provide some useful insights into the study of PPDM.

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Section: Discussionmentioning

confidence: 99%

Information Security in Big Data: Privacy and Data Mining

et al. 2014

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Privacy Implications of Privacy Settings and Tagging in Facebook

Damen

Zannone

2014

Lecture Notes in Computer Science

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Geographic Partitioning Techniques for the Anonymization of Health Care Data

Croft

Shi

Sack

et al.

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With large volumes of detailed health care data being collected, there is a high demand for the release of this data for research purposes. Hospitals and organizations are faced with conflicting interests of releasing this data and protecting the confidentiality of the individuals to whom the data pertains. Similarly, there is a conflict in the need to release precise geographic information for certain research applications and the requirement to censor or generalize the same information for the sake of confidentiality. Ultimately the challenge is to anonymize data in order to comply with government privacy policies while reducing the loss in geographic information as much as possible. In this paper, we present a novel geographic-based system for the anonymization of health care data. This system is broken up into major components for which different approaches may be supplied. We compare such approaches in order to make recommendations on which of them to select to best match user requirements. trade-off between the level of protection that can be achieved on a data set and the resultant utility of the data [13,14,17,34]. Although it is desirable to minimize the loss of any type of information in the data set, in some cases the preservation of geographic information may be of particular interest. Studies which involve the propagation of diseases across geographic areas require a high level of precision in the geographic information of the data set [23]. Any form of location-critical research such as spatial epidemiology requires high precision geographic information in order to be carried out [5,28,36]. However, the release of these precise geographic details greatly increases the risk of disclosure of confidential information due to higher levels of distinctness in the records of the data set. This risk creates a barrier in the disclosure of essential geographic information.In this paper, we present a novel and configurable system to achieve k-anonymity [34,35] on a data set through the use of geographic partitioning guided by the use of Voronoi diagrams [22]. This system, namedVoronoi-Based Aggregation System (VBAS), achieves anonymity in a data set through the generalization (coarsening of the level of precision) of geographic attributes and the suppression of records. By aggregating regions, we avoid the need for the suppression of small regions, which can lead to heavily censored data sets [10,20], while maintaining a higher degree of geographic precision than other methods (such as cropping [7,21]).Since any loss in geographic information has negative effects on the ability to effectively analyze a data set, we postulate it is desirable to preserve as much geographic information as possible [27]. VBAS addresses this problem by aggregating small regions of fine granularity into larger regions that satisfy criteria for achieving a sufficient level of anonymity while reducing the loss of geographic information. In order to evaluate the quality of the resultant aggregation, we employ measures of suppression an...

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The (P, α, K) anonymity model for privacy protection of personal information in the social networks

Cited by 3 publications

References 5 publications

Information Security in Big Data: Privacy and Data Mining

Information Security in Big Data: Privacy and Data Mining

Privacy Implications of Privacy Settings and Tagging in Facebook

Geographic Partitioning Techniques for the Anonymization of Health Care Data

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