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Privacy Preserving Classification of Biomedical Data With Secure Removing of Duplicate Records

Privacy Preserving Classification of Biomedical Data With Secure Removing of Duplicate Records

Boudheb Tarik (EEDIS Laboratory, Djillali Liabes University, Sidi Bel-Abbes, Algeria) and Elberrichi Zakaria (EEDIS Laboratory, Djillali Liabes University, Sidi Bel-Abbes, Algeria)
Copyright: © 2018 |Pages: 18
DOI: 10.4018/IJOCI.2018070104
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Abstract

Classifying data is to automatically assign predefined classes to data. It is one of the main applications of data mining. Having complete access to all data is critical for building accurate models. Data can be highly sensitive, such as biomedical data, which cannot be disclosed or shared with third party, because it can harm individuals and organizations. The challenge is how to preserve privacy and usefulness of data. Privacy preserving classification addresses this problem. Collaborative models are constructed over networks without violating the data owners' privacy. In this article, the authors address two problems: privacy records deduplication of the same records and privacy-preserving classification. They propose a randomized hash technic for deduplication and an enhanced privacy preserving classification of biomedical data over horizontally distributed data based on two homomorphic encryptions. No private, intermediate or final results are disclosed. Experimentations show that their solution is efficient and secure without loss of accuracy.
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2. State Of The Art

Data preprocessing is a vital task for data mining. It is mainly important for making data appropriate, e.g., avoiding duplicate records, estimating missing data, selecting best attributes, etc. Deduplication is a field of the preprocessing step. It can be local (centralized database), or over distributed data (multiple databases). Removal and prevention of duplication is an essential part of the security (Chang and Ramachandran, 2016). According to the authors (Yigzaw et al., 2017), duplicate records may lead to incorrect statistical results. Therefore, to increase the accuracy of analysis, deduplication is important.

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