Data Security and Security Data: 27th British National by Ken Barker (auth.), Lachlan M. MacKinnon (eds.)

By Ken Barker (auth.), Lachlan M. MacKinnon (eds.)

This e-book constitutes the completely refereed post-conference lawsuits of the twenty seventh British nationwide convention on Databases, BNCOD 27, held in Dundee, united kingdom, in June 2010. the ten revised complete papers and six brief papers, provided including three invited papers, 1 top paper of the linked occasion on instructing, studying and review of Databases (TLAD), and a couple of PhD discussion board top papers have been conscientiously reviewed and chosen from forty two submissions. unique concentration of the convention has been "Data defense and safety information" and so the papers conceal quite a lot of themes reminiscent of info safety, privateness and belief, safeguard info, information integration and interoperability, info administration for ubiquitous and cellular computing, info mining and knowledge extraction, facts modelling and architectures, information provenance, dataspaces, information streaming, databases and the grid, dispensed details structures, digital trade, company structures, heterogeneous databases, commercial functions, infrastructures and platforms, intermittently hooked up facts, dossier entry equipment and index constructions, coping with legacy information, new purposes and strategies, parallel and disbursed databases, peer-to-peer facts administration, functionality modelling of ubiquitous info use, own information administration, question and manipulation languages, question processing and optimisation, medical purposes, semantic net and ontologies, semi-structured info, metadata and xml, person interfaces and information visualisation, net information administration and deep internet, net companies, and workflow aid systems.

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Additional info for Data Security and Security Data: 27th British National Conference on Databases, BNCOD 27, Dundee, UK, June 29 - July 1, 2010. Revised Selected Papers

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A typical example of the group-based anonymization is so-called k-anonymity [20]. Its main idea is to ensure that every attribute values combination corresponds to at least k respondents in the dataset. To achieve that, different methods can be used, mentioned in [9, 10, 11] being the most popular. For the last 4-5 years, WT has also been used for providing data anonymity, though it has widely been used mainly in signal processing [21, 22] before. Paper [23] presents a good overview of applying wavelets to data mining in general.

In the paper, we present a novel method of using wavelet transform for providing group anonymity; it is gained through redistributing wavelet approximation values, along with simultaneous fixing data mean value and leaving wavelet details unchanged (or proportionally altering them). Moreover, we provide a comprehensive example to illustrate the method. Keywords: wavelet transform, group anonymity, statistical disclosure control, privacy-preserving data mining. 1 Introduction The problem of preserving privacy has become pressing in the recent years, and this fact doesn't seem to change in the nearest future.

Wellesley-Cambridge Press, Wellesley (1997) 23. : A Survey on Wavelet Applications in Data Mining. ACM SIGKDD Explorations Newsletter 4(2), 49–68 (2002) 24. : A Wavelet-based Approach to Preserve Privacy for Classification Mining. Decision Sciences Journal 37(4), 623–642 (2006) 25. : Wavelet-based Data Perturbation for Simultaneous PrivacyPreserving and Statistics-Preserving. In: 2008 IEEE International Conference on Data Mining Workshops, pp. 27–35. edu Abstract. One of the most widely used models for large-scale data mining is the k-nearest neighbor (k-nn) algorithm.

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