Implementation of Cryptography for Privacy Preserving Data Mining

Sharma, Anand; Ojha, Vibha

doi:10.5121/ijdms.2010.2306

Cited by 17 publications

(6 citation statements)

References 16 publications

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“…An attractive novel trend for data mining research is the development of techniques that integrates privacy concerns [15], [16], [17], [18]. Two current central categories to perform data mining tasks without compromising privacy are Perturbation method [19], [20], [21] and the secure computation method [22], [23], [24], [25]. By including any privacy preserving technique to data mining, the communication and computation cost will not be increased.…”

Section: Privacy Preserving Of Sensitive Cluster Data Objectsmentioning

confidence: 99%

Privacy Preserving Clustering Based on Linear Approximation of Function

Pasupuleti¹,

Narsimha

2013

IJCT

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Clustering analysis initiativesÂ a new direction in data mining that has major impact in various domains including machine learning, pattern recognition, image processing, information retrieval and bioinformatics. Current clustering techniques address some of theÂ requirements not adequately and failed in standardizing clustering algorithms to support for all real applications. Many clustering methods mostly depend on user specified parametric methods and initial seeds of clusters are randomly selected byÂ user.Â In this paper, we proposed new clustering method based on linear approximation of function by getting over all idea of behavior knowledge of clustering function, then pick the initial seeds of clusters as the points on linear approximation line and perform clustering operations, unlike grouping data objects into clusters by using distance measures, similarity measures and statistical distributions in traditional clustering methods. We have shown experimental results as clusters based on linear approximation yields goodÂ results in practice with an example ofÂ business data are provided.Â It alsoÂ explains privacy preserving clusters of sensitive data objects.

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Section: Privacy Preserving Of Sensitive Cluster Data Objectsmentioning

confidence: 99%

Privacy Preserving Clustering Based on Linear Approximation of Function

Pasupuleti¹,

Narsimha

2013

IJCT

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“…They used oblivious transfer protocol for secure computation. Their work has many advantages such as trust, correctness, efficiency, and fairness [5].…”

Section: Related Workmentioning

confidence: 99%

Privacy Preserving Fuzzy Based Decision Tree Classifier

Amma

Dhanaseelan

2017

IJARCSSE

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Huge volumes of personal data are collected and sharing of these data is beneficial for machine learning and data mining applications. These data are very much important for decision making in the competitive business world. Therefore, privacy preserving has been developed to hide sensitive data. We present a privacy preserving fuzzy based decision tree classifier, which allows extracting rules from the decision tree and the sensitive data are fuzzified in order to preserve the privacy. The objective of privacy preserving data classification is to build accurate classifier without revealing private information in the data being mined. In this paper, the advantages of fuzzy logic and decision tree are used to build the privacy preserving classifier. Fuzzy logic is used to preserve the privacy in the system and decision tree is used for classification. The effectiveness of the classifier is verified by experiments on Hepatitis dataset provided by the University of California, Irvine (UCI) machine learning repository. The classification accuracy obtained using this approach is 83.63%.

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“…Anand Sharma [13] describes in his paper the various types of cryptographic techniques as association rules, clustering or classification etc which are being implemented to achieve remarkable results. Special functions are being computed to store the sensitive data and providing access to stored data based on individual's role, thus the privacy is taken as main concern.…”

confidence: 99%

A Hybrid approach for Privacy Preservation of Big data

2017

IJRASET

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Now a days privacy preservation gains attention in order for secure transmission of data over the internet. A large amount of data is published every year for analytical and research purposes thus, it is important to use data mining. There are various privacy preserving data mining techniques as perturbation, anonymization, randomization, cryptography but still leave gaps in maintain privacy. Thus, it is necessary to propose a new technique termed as hybrid technique that fills the gaps and maintains privacy.

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Implementation of Cryptography for Privacy Preserving Data Mining

Abstract: ABSTARCT

Cited by 17 publications

References 16 publications

Privacy Preserving Clustering Based on Linear Approximation of Function

Privacy Preserving Clustering Based on Linear Approximation of Function

Privacy Preserving Fuzzy Based Decision Tree Classifier

A Hybrid approach for Privacy Preservation of Big data

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