Toward standardization in privacy-preserving data mining (original) (raw)

A Brief Study of Privacy-Preserving Practices (PPP) in Data Mining

arXiv (Cornell University), 2023

Data mining is the way toward mining fascinating patterns or information from an enormous level of the database. Data mining additionally opens another risk to privacy and data security.One of the maximum significant themes in the research fieldis privacy-preserving DM (PPDM). Along these lines, the investigation of ensuring delicate information and securing sensitive mined snippets of data without yielding the utility of the information in a dispersed domain.Extracted information from the analysis can be rules, clusters, meaningful patterns, trends or classification models. Privacy breach occur at some stage in the communication of data and aggregation of data. So far, many effective methods and techniques have been developed for privacy-preserving data mining, but yields into information loss and side effects on data utility and data mining effectiveness downgraded. In the focal point of consideration on the viability of Data Mining, Privacy and rightness should be improved and to lessen the expense.

A framework for evaluating privacy preserving data mining algorithms*

2005

Abstract Recently, a new class of data mining methods, known as privacy preserving data mining (PPDM) algorithms, has been developed by the research community working on security and knowledge discovery. The aim of these algorithms is the extraction of relevant knowledge from large amount of data, while protecting at the same time sensitive information.

A survey on privacy preserving data mining

… International Workshop on Database …, 2009

Privacy preserving data mining has become increasingly popular because it allows sharing of privacy sensitive data for analysis purposes .So people have become increasingly unwilling to share their data, frequently resulting in individuals either refusing to share their data or providing incorrect data. In recent years, privacy preserving data mining has been studied extensively, because of the wide proliferation of sensitive information on the internet. We discuss method for randomization, kanonymization, and distributed privacy preserving data mining. Knowledge is supremacy and the more knowledgeable we are about information break-in, we are less prone to fall prey to the evil hacker sharks of information technology. In this paper, we provide a review of the state-of-the-art methods for privacy and analyze the representative technique for privacy preserving data mining and points out their merits and demerits. Finally the present problems and directions for future research are discussed.

Remodeling: improved privacy preserving data mining (PPDM)

International journal of information technology, 2020

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.

A Case Study on Issues In Privacy Preserving Data Mining

The development in data mining technology brings serious threat to the individual information. The objective of privacy preserving data mining (PPDM) is to safeguard the sensitive information contained in the data. The unwanted disclosure of the sensitive information may happen during the process of data mining results. In this study we identify four different types of users involved in mining application i.e. data source provider, data receiver, data explorer and determiner decision maker. We would like to provide useful insights into the study of privacy preserving data mining. This paper presents a comprehensive noise addition technique for protecting individual privacy in a data set used for classification, while maintaining the data quality. We add noise to all attributes, both numerical and categorical, and both to class and non-class, in such a way so that the original patterns are preserved in a perturbed data set. Our technique is also capable of incorporating previously proposed noise addition techniques that maintain the statistical parameters of the data set, including correlations among attributes. Thus the perturbed data set may be used not only for classification but also for statistical analysis.

An Enhanced Approach for Privacy Preserving Data Mining (PPDM)

International Journal of Recent Trends in Engineering and Research, 2018

With the development of network, data collection and storage technology, the use and sharing of large amounts of data has become possible. Once the data and information accumulated, it will become the wealth of information. However, traditional data mining techniques and algorithms directly operated on the original data set, which will cause the leakage of privacy data. At the same time, large amounts of data implicate the sensitive knowledge that their disclosure cannot be ignored to the competitiveness of enterprise. In order to overcome these problems, Privacy Preserving Data Mining (PPDM) techniques are developed. Traditional PPDM techniques suffer from different types of attacks and loss of information. In this paper an alternative method was proposed which provides less information loss and more privacy.

IJERT-An Enhanced Approach to Privacy-Preserving in Data Mining and its Techniques

International Journal of Engineering Research and Technology (IJERT), 2015

https://www.ijert.org/an-enhanced-approach-to-privacy-preserving-in-data-mining-and-its-techniques https://www.ijert.org/research/an-enhanced-approach-to-privacy-preserving-in-data-mining-and-its-techniques-IJERTV4IS020595.pdf The Privacy preserving Data mining (PPDM) has been among the important issues of current research that deals with preserving privacy of individual's data over a network. The major area of concern is that non-sensitive data even may deliver sensitive information, including personal information, facts or patterns. In this paper, we present a unique concept of combining different PPDM techniques which provides high level security and integrity to confidential data. This paper mainly highlights the improved results that can be obtained on merging the two different PPDM techniques. One of the latest concept of PPDM called Slicing has also been explained in our paper. It has been observed that slicing preserves better data utility and thus we have tried to merge slicing with one of the best security mechanism that is Cryptography.

An overview of privacy preserving data mining

Crossroads, 2009

As it becomes evident, there exists an extended set of application scenarios in which information or knowledge derived from the data must be shared with other (possibly untrusted) entities. The sharing of data and/or knowledge may come at a cost to privacy, primarily due to two reasons:

GRA -GLOBAL RESEARCH ANALYSIS X 27 Security And Privacy Challenges in Data Mining

given the rising privacy concerns, the data mining community has faced a new challenge. Having shown how effective its tools are in revealing the knowledge locked within huge databases, it is now required to develop methods that restrain the power of these tools to protect the privacy of individuals. The question how these two contrasting goals, mining new knowledge while protecting individuals' privacy, can be reconciled, is the focus of this research. We seek ways to improve the tradeoff between privacy and utility when mining data.

Classification of Privacy Preserving Data Mining Algorithms: A Review

Jurnal Elektronika dan Telekomunikasi

Nowadays, data from various sources are gathered and stored in databases. The collection of the data does not give a significant impact unless the database owner conducts certain data analysis such as using data mining techniques to the databases. Presently, the development of data mining techniques and algorithms provides significant benefits for the information extraction process in terms of the quality, accuracy, and precision results. Realizing the fact that performing data mining tasks using some available data mining algorithms may disclose sensitive information of data subject in the databases, an action to protect privacy should be taken into account by the data owner. Therefore, privacy preserving data mining (PPDM) is becoming an emerging field of study in the data mining research group. The main purpose of PPDM is to investigate the side effects of data mining methods that originate from the penetration into the privacy of individuals and organizations. In addition, it gu...