Privacy Preserving Data Clustering Using a Heterogeneous Data Distortion

Padala, Preethi; Kumar, Kintali Pavan; Ullhaq, Mohammed Reezwan; Naveen, A; Hyma, J.

doi:10.1007/978-981-13-1927-3_51

Cited by 2 publications

(2 citation statements)

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“…Privacy preserving data mining is an important research area with the goal of ensuring the privacy of individuals while enabling to perform data mining on personal data. Many privacy preserving techniques such as privacy preserving association rule mining, privacy preserving clustering (Inan et al, 2007;Prabha & Vijayarani, 2011;Preethi, Kumar, Ullhaq, Naveen, & Janapana, 2019), privacy preserving classification using various data mining algorithms such as SVM, decision trees, Naïve Bayes, k-NN, and so on (Kantarcıo glu & Clifton, 2004;Liu, Kantarcioglu, & Thuraisingham, 2008, 2009Tsang, Kao, Yip, Ho, & Lee, 2009) have been studied. On the other hand, differential privacy has been recently proposed method to guarantee strong privacy and it has been widely used for privacy preserving classification in the literature.…”

Section: Differentially Private Classificationmentioning

confidence: 99%

Privacy preserving classification over differentially private data

Zorarpacı

Özel

2020

WIREs Data Min & Knowl

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Privacy preserving data classification is an important research area in data mining field. The goal of a privacy preserving classification algorithm is to protect the sensitive information as much as possible, while providing satisfactory classification accuracy. Differential privacy is a strong privacy guarantee that enables privacy of sensitive data stored in a database by determining the ratio of sensitive information leakage with respect to an ɛ parameter. In this study, our aim is to investigate the classification performance of the state‐of‐the‐art classification algorithms such as C4.5, Naïve Bayes, One Rule, Bayesian Networks, PART, Ripper, K*, IBk, and Random tree for performing privacy preserving classification. To preserve privacy of the data to be classified, we applied input perturbation technique coming from differential privacy, and observed the relationship between the ɛ parameter values and accuracy of the classifiers. To our best knowledge, this article is the first study that analyzes the performances of the well‐known classification algorithms over differentially private data, and discovers which datasets are more suitable for privacy preserving classification when input perturbation is applied to provide data privacy. The classification algorithms are compared by using the differentially private versions of the well‐known datasets from the UCI repository. According to the experimental results, we observed that, as ɛ parameter value increases, better classification accuracies are achieved with lower privacy levels. When the classifiers are compared, Naïve Bayes classifier is the most successful method. The ɛ parameter should be greater than or equal to 2 (i.e., ɛ ≥2) to achieve cloud server is malicious and untrusted, sensitive data will satisfactory classification accuracies. This article is categorized under: Commercial, Legal, and Ethical Issues > Security and Privacy Technologies > Classification

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Section: Differentially Private Classificationmentioning

confidence: 99%

Privacy preserving classification over differentially private data

Zorarpacı

Özel

2020

WIREs Data Min & Knowl

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“…The goal of the privacy preserving data mining is to ensure the privacy of individuals while enabling to perform data mining techniques. Many privacy preserving techniques such as privacy preserving association rule mining, privacy preserving clustering [13,14,15,16], privacy preserving classification relying on a number of data mining algorithms such as SVM, k-NN etc. [17,18] have been studied.…”

Section: Classification With Differential Privacymentioning

confidence: 99%

A hybrid approach of homomorphic encryption and differential privacy for privacy preserving classification

Zorarpacı¹,

Özel²

2020

International Journal of Applied Mathematics Electronics and Computers

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Privacy preserving data mining is a substantial research area that aims at protecting the privacy of individuals while enabling to perform data mining techniques. In this study, we propose a secure protocol that fulfils the privacy restriction by combining homomorphic encryption with differential privacy and integrate this protocol into Holte's One Rule which is a simple, but accurate and efficient classification algorithm. The proposed method allows a researcher to get the answers of his/her queries to build One Rule classifier by processing the encrypted training dataset under Paillier's cryptosystem and also applies differential privacy to minimize the privacy leakage of individuals as much as possible in this training dataset. Therefore, both of security and privacy of the individuals in the training dataset for classification are provided thanks to our proposed method; since neither the parties, nor the researcher attain any information about the individuals in the database. Besides the One Rule classifier, we apply our proposed privacy preservation model to Naïve Bayes classification algorithm for the performance comparison, and show the efficiency of the proposed method through experiments on real data from UCI repository.

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Privacy Preserving Data Clustering Using a Heterogeneous Data Distortion

Cited by 2 publications

References 1 publication

Privacy preserving classification over differentially private data

Privacy preserving classification over differentially private data

A hybrid approach of homomorphic encryption and differential privacy for privacy preserving classification

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