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Enhanced P-Sensitive K-Anonymity Models for Privacy Preserving Data Publishing
Xiaoxun Sun,Hua Wang,Jiuyong Li,Traian Marius Truta
Transactions on Data Privacy , 2008,
Abstract: Publishing data for analysis from a micro data table containing sensitive attributes, while maintaining individual privacy, is a problem of increasing significance today. The k-anonymity model was proposed for privacy preserving data publication. While focusing on identity disclosure, k-anonymity model fails to protect attribute disclosure to some extent. Many efforts are made to enhance the k-anonymity model recently. In this paper, we propose two new privacy protection models called (p, a)-sensitive k-anonymity and (p^{+}, a)-sensitive k-anonymity, respectively. Different from previous the p-sensitive k-anonymity model, these new introduced models allow us to release a lot more information without compromising privacy. Moreover, we prove that the (p, a)-sensitive and (p^{+}, a)-sensitive k-anonymity problems are NP-hard. We also include testing and heuristic generating algorithms to generate desired micro data table. Experimental results show that our introduced model could significantly reduce the privacy breach.
Identity-Reserved Anonymity in Privacy Preserving Data Publishing

TONG Yun-Hai,TAO You-Dong,TANG Shi-Wei,YANG Dong-Qing,

软件学报 , 2010,
Abstract: In the research of privacy preserving data publishing, the present method always removes the individual identification attributes and then anonymizes the quasi-identifier attributes. This paper analyzes the situation of multiple records one individual and proposes the principle of identity-reserved anonymity. This method reserves more information while maintaining the individual privacy. The generalization and loss-join approaches are developed to meet this requirement. The algorithms are evaluated in an experimental scenario, reserving more information and demonstrating practical applicability of the approaches.
Privacy-Preserving Data Publishing for Multiple Numerical Sensitive Attributes  [PDF]
Qinghai Liu,Hong Shen,Yingpeng Sang
- , 2015, DOI: 10.1109/TST.2015.7128936
Abstract: Anonymized data publication has received considerable attention from the research community in recent years. For numerical sensitive attributes, most of the existing privacy-preserving data publishing techniques concentrate on microdata with multiple categorical sensitive attributes or only one numerical sensitive attribute. However, many real-world applications can contain multiple numerical sensitive attributes. Directly applying the existing privacy-preserving techniques for single-numerical-sensitive-attribute and multiple-categorical-sensitive-attributes often causes unexpected disclosure of private information. These techniques are particularly prone to the proximity breach, which is a privacy threat specific to numerical sensitive attributes in data publication. In this paper, we propose a privacy-preserving data publishing method, namely MNSACM, which uses the ideas of clustering and Multi-Sensitive Bucketization (MSB) to publish microdata with multiple numerical sensitive attributes. We use an example to show the effectiveness of this method in privacy protection when using multiple numerical sensitive attributes.
Fuzzy-based methods for privacy preserving

WANG Qian,YANG Chuan-dong,LIU Hong,
王 茜
,杨传栋,刘 泓

计算机应用研究 , 2013,
Abstract: This paper did research based on fuzzy sets to overcome the high complexity, low efficiency and poor data availability of k-anonymity in the research of privacy-preserving data publishing. It focused on the processing of numerical attributes, and proposed the maximal membership degree algorithm. It fuzzed sensitive numerical attributes to semantic data which was released combining with membership degree. Verified through experiments, compared with k-anonymity methods, the MMD has better efficiency, furthermore, its information losses will be far smaller than k-anonymity and the availability of released data is better.
Research on Anonymity Technique for Personalization Privacy-preserving Data Publishing


计算机科学 , 2012,
Abstract: Privacy-preserving data publishing for personalization is a hot research topic in technique for controlling pri- vacy disclosure in data publishing in recent years. An overview of this area was given in this survey. First, on the basis of analyzing types of different personalization service, relevant anonymization models of personalization privacy were built. Second, according to the different adopting technictues, a summarization of the art of personalization privacy-pre- serving techniques was given, and the fundamental principles and characteristics of various techniques were generally de- scribed. In addition, some existing privacy measure methods and standards were provided, according to the differences of information measure of adopted algorithms. Finally, based on the comparison and analysis of existing researches, future research directions of privacy-preserving data publishing for personalization were forecasted.
Privacy preserving technology for multiple sensitive attributes in data publishing

LIU Shan-cheng,JIN Hu,JU Shi-guang,

计算机应用研究 , 2011,
Abstract: In view of the privacy leak problem of secure data publishing when sensitive data contains multi attributes,based on the multi-dimension bucket grouping approach,this paper proposed a(g,l)-grouping approach on the idea of lossy join.It divided sensitive attributes into groups according to the sensitivity,and set the size of each group as the dimension number of each dimension of the multi-dimension bucket.And proposed two specific line time based(g,l)-grouping algorithms,which were general(g,l)-grouping alg...
Privacy Preserving Technology for Multiple Sensitive Attributes in Medical Data Publishing

JIN Hua LIU Shan-cheng JU Shi-guang,

计算机科学 , 2011,
Abstract: In view of the privacy leak problem of secure data publishing when sensitive data contains multi-attributes,on the basis of analysing the multi dimension bucket approach,this paper proposed an 1-coverage clustering grouping approach based on the same sensitive attribute set and the idea of lossy join. Firstly it calculated the same sensitive attribute set of each record, and then we grouped each record which satisfies the constraints of L-coverage following the idea of clustering. Also we designed a LCCG algorithm to implement the approach. Experimental results on the real world datasets show that the new model is able to reduce privacy disclosure apparently and enforce security of data publishing.
Privacy-preserving Data Mining, Sharing and Publishing  [PDF]
Katarzyna Pasierb,Tomasz Kajdanowicz,Przemyslaw Kazienko
Computer Science , 2013,
Abstract: The goal of the paper is to present different approaches to privacy-preserving data sharing and publishing in the context of e-health care systems. In particular, the literature review on technical issues in privacy assurance and current real-life high complexity implementation of medical system that assumes proper data sharing mechanisms are presented in the paper.
Preserving privacy in social networks based on d-neighborhood subgraph anonymity

JIN Hua,ZHANG Zhi-xiang,LIU Shan-cheng,JU Shi-guang,

计算机应用研究 , 2011,
Abstract: Preserving privacy is very necessary for social network information publishing, because analysis of social networks can violate the individual privacy. This paper proposed a k-anonymity model of d-neighborhood subgraph described by matrix of supe-edge. It transformed the anonymization of subgraph into matching the matrix which represented the d-neighborhood subgraph of vertex, and ensured that the numbers of isomorphic d-neighborhood subgraph was no less than k for every vertex. Experimental results show that the proposed model can effectively resist neighborhood attacks and preserve privacy information.
Attacks on Anonymization-Based Privacy-Preserving: A Survey for Data Mining and Data Publishing  [PDF]
Abou-el-ela Abdou Hussien, Nermin Hamza, Hesham A. Hefny
Journal of Information Security (JIS) , 2013, DOI: 10.4236/jis.2013.42012

Data mining is the extraction of vast interesting patterns or knowledge from huge amount of data. The initial idea of privacy-preserving data mining PPDM was to extend traditional data mining techniques to work with the data modified to mask sensitive information. The key issues were how to modify the data and how to recover the data mining result from the modified data. Privacy-preserving data mining considers the problem of running data mining algorithms on confidential data that is not supposed to be revealed even to the party running the algorithm. In contrast, privacy-preserving data publishing (PPDP) may not necessarily be tied to a specific data mining task, and the data mining task may be unknown at the time of data publishing. PPDP studies how to transform raw data into a version that is immunized against privacy attacks but that still supports effective data mining tasks. Privacy-preserving for both data mining (PPDM) and data publishing (PPDP) has become increasingly popular because it allows sharing of privacy sensitive data for analysis purposes. One well studied approach is the k-anonymity model [1] which in turn led to other models such as confidence bounding, l-diversity, t-closeness, (α,k)-anonymity, etc. In particular, all known mechanisms try to minimize information loss and such an attempt provides a loophole for attacks. The aim of this paper is to present a survey for most of the common attacks techniques for anonymization-based PPDM & PPDP and explain their effects on Data Privacy.

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