Secure Building Blocks for Data Privacy

Secure Building Blocks for Data Privacy

Shuguo Han (Nanyang Technological University, Singapore)
Copyright: © 2009 |Pages: 6
DOI: 10.4018/978-1-60566-010-3.ch265
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Rapid advances in automated data collection tools and data storage technology have led to the wide availability of huge amount of data. Data mining can extract useful and interesting rules or knowledge for decision making from large amount of data. In the modern world of business competition, collaboration between industries or companies is one form of alliance to maintain overall competitiveness. Two industries or companies may find that it is beneficial to collaborate in order to discover more useful and interesting patterns, rules or knowledge from their joint data collection, which they would not be able to derive otherwise. Due to privacy concerns, it is impossible for each party to share its own private data with one another if the data mining algorithms are not secure. Therefore, privacy-preserving data mining (PPDM) was proposed to resolve the data privacy concerns while yielding the utility of distributed data sets (Agrawal & Srikant, 2000; Lindell.Y. & Pinkas, 2000). Conventional PPDM makes use of Secure Multi-party Computation (Yao, 1986) or randomization techniques to allow the participating parties to preserve their data privacy during the mining process. It has been widely acknowledged that algorithms based on secure multi-party computation are able to achieve complete accuracy, albeit at the expense of efficiency.
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Main Focus

In this section, we review current work on privacy-preserving data mining algorithms that are based on secure multi-party computation (Yao, 1986).

Privacy-Preserving Decision Trees

In (Lindell & Pinkas, 2000), the authors proposed a privacy-preserving ID3 algorithm based on cryptographic techniques for horizontally partitioned data involving two parties. The authors in (Du & Zhan, 2002) addressed the privacy-preserving decision tree induction problem for vertically partitioned data based on the computation of secure scalar product involving two parties. The scalar product is securely computed using a semi-trusted commodity server. In the model, a semi-trusted third party helps two parties to compute scalar product; the third party will learn nothing about the parties’ private data and is required not to collude with any of them. The authors in (Vaidya & Clifton, 2005a) extended the privacy-preserving ID3 algorithm for vertically partitioned data from two parties to multiple parties using the secure set intersection cardinality protocols.

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