Remodeling: improved privacy preserving data mining (PPDM)
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ORIGINAL RESEARCH
Remodeling: improved privacy preserving data mining (PPDM) Meghna D. Shastri1
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Anala A. Pandit1
Received: 17 February 2020 / Accepted: 28 September 2020 Bharati Vidyapeeth’s Institute of Computer Applications and Management 2020
Abstract The data provided by individuals and various organizations while using internet applications and mobile devices are very useful to generate solutions and create new opportunities. The data which is shared needs to be precise to get the quality results. The data which may contain an individual’s sensitive information cannot be revealed to the world without applying some privacy preserving technique on it. Privacy preserving data mining (PPDM) and Privacy preserving data publishing (PPDP) are some of the techniques which can be utilized to preserve privacy. There are some positives and negatives for every technique. The cons frequently constitute loss of data, reduction in the utility of data, compromised diversity of data, reduced security, etc. In this paper, the authors propose a new technique called Remodeling, which works in conjunction with the k-anonymity and K-means algorithm to ensure minimum data loss, better privacy preservation while maintaining the diversity of data. Network data security is also handled by this proposed model. In this research paper, theoretically, we have shown that the proposed technique addresses all the above-mentioned cons and also discusses the merits and demerits of the same. Keywords Remodeling Privacy preserving Anonymization Clustering Encryption Data mining
& Meghna D. Shastri [email protected] Anala A. Pandit [email protected] 1
Department of Master in Computer Applications, Veermata Jijabai Technological Institute Mumbai, Mumbai, India
1 Introduction The data provided by individuals and various organizations are very useful to generate solutions and create new opportunities like the invention of the new field. Especially, in the healthcare field, insights based on the data about patients might help the doctors with early detection and prevention of diseases. However, this type of data may contain sensitive information of an individual. If such sensitive information is shared or published without performing any data translation, it breaches personal privacy despite removing personal identifiers like name, SSN, etc.[1]. Privacy preserving data mining (PPDM) [10] and Privacy preserving data publishing (PPDP) [11] are some of the techniques which can be utilized to preserve privacy. To achieve PPDM, various models, algorithms, and techniques are explained in the literature [3, 6]. The main difference in PPDM and PPDP is that the first one focusses on retrieving relevant information from the huge amount of data using data mining while maintaining its privacy whereas the latter one focusses on publishing info collected from data providers instead of data mining outcome [2, 16–18]. The objective of privacy preserving is to build techniques in a secured/unsecured way, so that the data and the know
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