Privacy Preserving Tree Augmented Naïve Bayesian Multi-party Implementation on Horizontally Partitioned Databases

Skarkala, Maria Eleni; Μaragoudakis, Manolis; Gritzalis, Stefanos; Mitrou, Lilian

doi:10.1007/978-3-642-22890-2_6

Cited by 7 publications

(5 citation statements)

References 21 publications

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“…As mentioned, the current work is an extension of previous research [19,20]. Notably, some of the features and requirements used arise from the quotations presented by Mangos et al [53].…”

Section: Protocol Descriptionmentioning

confidence: 93%

“…In this paper, we present an extended version of the work originally presented at the SEEDA-CECNSM 2020 conference [19]. The privacy-preserving data mining approach was first introduced in Reference [20] only for horizontally partitioned databases. Here, we exploited the multi-candidate election schema [21] to extract global information from both horizontally and vertically partitioned statistical databases.…”

Section: Introductionmentioning

confidence: 99%

“…To the best of our knowledge, in the privacy-preserving research field, algorithms that support both horizontally and vertically partitioned databases have never been proposed. In this paper, we present a well-designed and improved privacy-preserving data mining technique, which was originally proposed in References [19,20], which focuses on preserving privacy and aims to obtain useful information from horizontally and vertically partitioned statistical databases, handling both nominal and numeric attributes values (including binary values).…”

Section: Introductionmentioning

confidence: 99%

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PPDM-TAN: A Privacy-Preserving Multi-Party Classifier

et al. 2021

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Distributed medical, financial, or social databases are analyzed daily for the discovery of patterns and useful information. Privacy concerns have emerged as some database segments contain sensitive data. Data mining techniques are used to parse, process, and manage enormous amounts of data while ensuring the preservation of private information. Cryptography, as shown by previous research, is the most accurate approach to acquiring knowledge while maintaining privacy. In this paper, we present an extension of a privacy-preserving data mining algorithm, thoroughly designed and developed for both horizontally and vertically partitioned databases, which contain either nominal or numeric attribute values. The proposed algorithm exploits the multi-candidate election schema to construct a privacy-preserving tree-augmented naive Bayesian classifier, a more robust variation of the classical naive Bayes classifier. The exploitation of the Paillier cryptosystem and the distinctive homomorphic primitive shows in the security analysis that privacy is ensured and the proposed algorithm provides strong defences against common attacks. Experiments deriving the benefits of real world databases demonstrate the preservation of private data while mining processes occur and the efficient handling of both database partition types.

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“…As mentioned, the current work is an extension of previous research [19,20]. Notably, some of the features and requirements used arise from the quotations presented by Mangos et al [53].…”

Section: Protocol Descriptionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

PPDM-TAN: A Privacy-Preserving Multi-Party Classifier

et al. 2021

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“…Keshavamurthy et al [18] propose secure multi-party computation-based approach with trusted third party to compute the aggregate class instances for VPD using the probabilistic model of classifier such as naïve Bayes technique. In another study [28], privacy-preserving tree augmented NBC is offered for statistical databases that are horizontally partitioned. In the proposed method, privacy is ensured by multi-candidate election scheme.…”

Section: Related Workmentioning

confidence: 99%

Estimating NBC-based recommendations on arbitrarily partitioned data with privacy

Yakut

Polat

2012

Knowledge-Based Systems

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“…distributions of sensitive attributes) are desired. Skarkala et al [77] also study the privacy-preserving classification problem for horizontally partitioned data. They propose a privacy-preserving version of the tree augmented naïve (TAN) Bayesian classifier [78] to extract global information from horizontally partitioned data.…”

Section: B: Naïve Bayesian Classificationmentioning

confidence: 99%

Information Security in Big Data: Privacy and Data Mining

et al. 2014

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The growing popularity and development of data mining technologies bring serious threat to the security of individual's sensitive information. An emerging research topic in data mining, known as privacypreserving data mining (PPDM), has been extensively studied in recent years. The basic idea of PPDM is to modify the data in such a way so as to perform data mining algorithms effectively without compromising the security of sensitive information contained in the data. Current studies of PPDM mainly focus on how to reduce the privacy risk brought by data mining operations, while in fact, unwanted disclosure of sensitive information may also happen in the process of data collecting, data publishing, and information (i.e., the data mining results) delivering. In this paper, we view the privacy issues related to data mining from a wider perspective and investigate various approaches that can help to protect sensitive information. In particular, we identify four different types of users involved in data mining applications, namely, data provider, data collector, data miner, and decision maker. For each type of user, we discuss his privacy concerns and the methods that can be adopted to protect sensitive information. We briefly introduce the basics of related research topics, review state-of-the-art approaches, and present some preliminary thoughts on future research directions. Besides exploring the privacy-preserving approaches for each type of user, we also review the game theoretical approaches, which are proposed for analyzing the interactions among different users in a data mining scenario, each of whom has his own valuation on the sensitive information. By differentiating the responsibilities of different users with respect to security of sensitive information, we would like to provide some useful insights into the study of PPDM.

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Privacy Preserving Tree Augmented Naïve Bayesian Multi-party Implementation on Horizontally Partitioned Databases

Cited by 7 publications

References 21 publications

PPDM-TAN: A Privacy-Preserving Multi-Party Classifier

PPDM-TAN: A Privacy-Preserving Multi-Party Classifier

Estimating NBC-based recommendations on arbitrarily partitioned data with privacy

Information Security in Big Data: Privacy and Data Mining

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