SVD based Data Transformation Methods for Privacy Preserving Clustering

Lakshmi, M.; Rani, K. Sandhya

doi:10.5120/13473-1157

Cited by 7 publications

(2 citation statements)

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“…This process runs in an unsupervised manner. Similar to classification, current researches on privacy-preserving clustering can be roughly categorized into two types, based on data transformation [ 57 , 58 ] and based on secure multiparty computation [ 59 , 60 ].…”

Section: Tasks and Methodsmentioning

confidence: 99%

Privacy Protection and Secondary Use of Health Data: Strategies and Methods

Xiang

Cai

2021

BioMed Research International

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Health big data has already been the most important big data for its serious privacy disclosure concerns and huge potential value of secondary use. Measurements must be taken to balance and compromise both the two serious challenges. One holistic solution or strategy is regarded as the preferred direction, by which the risk of reidentification from records should be kept as low as possible and data be shared with the principle of minimum necessary. In this article, we present a comprehensive review about privacy protection of health data from four aspects: health data, related regulations, three strategies for data sharing, and three types of methods with progressive levels. Finally, we summarize this review and identify future research directions.

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Section: Tasks and Methodsmentioning

confidence: 99%

Privacy Protection and Secondary Use of Health Data: Strategies and Methods

Xiang

Cai

2021

BioMed Research International

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“…The methods proposed in [84] deal with numerical attributes, while in [84], Rajalaxmi and Natarajan propose a set of hybrid data transformations for categorical attributes. Recently, Lakshmi and Rani [85] propose two hybrid methods to hide the sensitive numerical attributes. The methods utilize three different techniques, namely singular value decomposition (SVD), rotation data perturbation and independent component analysis.…”

Section: ) Privacy-preserving Clusteringmentioning

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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