State-of-the-art in privacy preserving data mining

Verykios, Vassilios S.; Bertino, Elisa; Fovino, Igor Nai; Provenza, Loredana Parasiliti; Saygın, Yücel; Theodoridis, Yannis

doi:10.1145/974121.974131

Cited by 653 publications

(341 citation statements)

References 23 publications

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“…Privacy-preserving record linkage with exact matching is very similar to privacy-preserving set intersection: to identify the intersection of two sets of records without revealing private records. Surveys could be found at [26,11]. Among the more popular approaches, solutions based on homomorphic encryption (e.g.…”

Section: Related Workmentioning

confidence: 99%

Privacy Preserving Group Linkage

Chen

Luo

et al. 2011

Lecture Notes in Computer Science

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Abstract. The problem of privacy-preserving record linkage is to find the intersection of records from two parties, while not revealing any private records to each other. Recently, group linkage has been introduced to measure the similarity of groups of records [19]. When we extend the traditional privacy-preserving record linkage methods to group linkage measurement, group membership privacy becomes vulnerable -record identity could be discovered from unlinked groups. In this paper, we introduce threshold privacy-preserving group linkage (TPPGL) schemes, in which both parties only learn whether or not the groups are linked. Therefore, our approach is secure under group membership inference attacks. In experiments, we show that using the proposed TPPGL schemes, group membership privacy is well protected against inference attacks with a reasonable overhead.

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Section: Related Workmentioning

confidence: 99%

Privacy Preserving Group Linkage

Chen

Luo

et al. 2011

Lecture Notes in Computer Science

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“…Kantarcioglu and Clifton [7] suggested that adopting a common framework for discussing privacy preservation will enable next generation data mining technology to make substantial advances in alleviating privacy concerns. Verykios et al [28] analyzed the state-ofthe-art, classified the proposed algorithms from five different dimensions: data distribution, data modification, data mining algorithm, data or rule hiding, and privacy preservation. They also suggested a set of metrics for assessing PPDM performance.…”

Section: Related Workmentioning

confidence: 99%

Privacy Preserving Data Mining Research: Current Status and Key Issues

Chu

Wang

et al. 2007

Computational Science – ICCS 2007

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Abstract. Recent advances in the Internet, in data mining, and in security technologies have gave rise to a new stream of research, known as privacy preserving data mining (PPDM). PPDM technologies allow us to extract relevant knowledge from a large amount of data, while hide sensitive data or information from disclosure. Several research questions have often being asked:(1) what kind of option available for privacy preserving? (2) Which method is more popular? (3) how to measure the performance of these algorithms? And (4) how effective of these algorithms in preserving privacy? To help answer these questions, we conduct an extensive review of 29 recent references from years 2000 to 2006 for analysis.

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“…PPDM is concerned with extraction of information from data warehouse without revealing sensitive information of individuals and company privacy details [8], [9], [10]. Present industry consists of database which is distributed across multiple source locations.…”

Section: Introductionmentioning

confidence: 99%

An Extended Conceptual Modeling for ETL Processes in Privacy Preserving Data Mining

P¹,

Kumar²,

Kavya³

2012

IJFCC

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Abstract-Extraction transformation load is an important stage of a data warehouse process. It involves retrieval of information from multiple sources, applying transformation to map with the schema of the data warehouse. Privacy preserving data mining has become a prerequisite for all the present data warehouse projects. In this paper we provide a foundation on embedding privacy to conceptual modeling by indicating additional notations which ensures faster design.

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State-of-the-art in privacy preserving data mining

Cited by 653 publications

References 23 publications

Privacy Preserving Group Linkage

Privacy Preserving Group Linkage

Privacy Preserving Data Mining Research: Current Status and Key Issues

An Extended Conceptual Modeling for ETL Processes in Privacy Preserving Data Mining

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