Privacy-preserving data mining: A feature set partitioning approach

Matatov, Nissim; Rokach, Lior; Maimon, Oded

doi:10.1016/j.ins.2010.03.011

Cited by 86 publications

(46 citation statements)

References 53 publications

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“…Table 4 dataset is given a input to algorithm 1, it processes the dataset by considering randomly generated values of Probability matrix (T) and Mapping matrix (M) and produces a converted table C as shown in table 5. The performance of the proposed methodology is evaluated in terms of two data metrics namely information loss [11] and privacy gain. The following formulae are used to measure information loss ILoss and privacy gain.…”

Section: Resultsmentioning

confidence: 99%

An Enhanced Approach for Privacy Preserving Data Mining (PPDM)

2018

IJRTER

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Abstract-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.

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Section: Resultsmentioning

confidence: 99%

An Enhanced Approach for Privacy Preserving Data Mining (PPDM)

2018

IJRTER

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“…The experiments have been evaluated and comparison results with the set of data are done with the existing DMPD approach [3]. The below table and graph describes the performance result of the proposed FC with the existing DMPD approach.…”

Section: Resultsmentioning

confidence: 99%

“…The most widespread technique for achieving observance with kanonymity is to restore definite values with fewer specific but semantically reliable values. In [3], the author proposed a diverse technique for determining k-anonymity by splitting up the unusual dataset into numerous ridges such that all one of them sticks to k-anonymity. In [4], the author explained numerous features of the technique.…”

Section: Literature Reviewmentioning

confidence: 99%

Bucketization based Flow Classification Algorithm for Data Stream Privacy Mining

Kesavaraj¹,

Sukumaran²

2013

IJCA

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In recent years, data mining plays a major role in maintaining the huge volume of data from which it can derive the useful information. With the huge number of formation of data, the data wants to be lectured in a limit to the charge of growth. But it is complex to get over the set of meaningful information from the continuous set of data. Data-stream mining is a method which can discover important information from a huge contract of prehistoric data. For identification of useful information, the classification of continuous data streams is done. Current approaches in classifying the data streams are processed using supervised learning algorithms, which can be qualified with tagged data. Usually, manual classification of data is both expensive and time consuming. As a result, where massive amount of data emerge at a high speed, tagged data might be very sparse. Therefore, only a restricted amount of training data might be accessible for constructing the classification models, tend to badly trained classifiers. To overcome the issue, in this work, a novel technique is presented to build a classification set having both unlabeled and a small amount of labeled instances. This model is built by using the Flow Classification Algorithm (FCA). The FC algorithm is able to judge internally on set of marked data. Before classification, the correlation set of attributes in the each record set are grouped using bucketization technique. The superiority of models updated from them is enough for utilization of unlabeled records, or whether more set of labeled records are needed for classification is processed. Experimental evalaution is conducted to the proposed FC technqiue over its counterparts to find a set of diverse solution in terms of execution time, classification accuracy and security. Performance metrics for evaluation of proposed FCA technique shows that the security level is 10-15% high against existing work.

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“…Given the importance of understanding the term data mining, we will list a few explanations and definitions of the said term referring to the eminent names in the field. One such definition is given and an article published in the International Journal of Computer Trends and Technology (Matatov et al, 2010). From the previous definition it can be concluded that data mining includes a wide range of tools which in a very short time provide very useful and specific information that can form the basis for making managerial or other decisions.…”

Section: Data Miningmentioning

confidence: 99%

Data Mining Usage in Corporate Information Security: Intrusion Detection Applications

Quhtani

2017

Business Systems Research Journal

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Background: The globalization era has brought with it the development of high technology, and therefore new methods of preserving and storing data. New data storing techniques ensure data are stored for longer periods of time, more efficiently and with a higher quality, but also with a higher data abuse risk. Objective: The goal of the paper is to provide a review of the data mining applications for the purpose of corporate information security, and intrusion detection in particular. Methods/approach: The review was conducted using the systematic analysis of the previously published papers on the usage of data mining in the field of corporate information security. Results: This paper demonstrates that the use of data mining applications is extremely useful and has a great importance for establishing corporate information security. Data mining applications are directly related to issues of intrusion detection and privacy protection. Conclusions: The most important fact that can be specified based on this study is that corporations can establish a sustainable and efficient data mining system that will ensure privacy and successful protection against unwanted intrusions.

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Privacy-preserving data mining: A feature set partitioning approach

Cited by 86 publications

References 53 publications

An Enhanced Approach for Privacy Preserving Data Mining (PPDM)

An Enhanced Approach for Privacy Preserving Data Mining (PPDM)

Bucketization based Flow Classification Algorithm for Data Stream Privacy Mining

Data Mining Usage in Corporate Information Security: Intrusion Detection Applications

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