Abstract
Privacy-preserving data mining (PPDM) is one of the newest trends in privacy and security research. It is driven by one of the major policy issues of the information era: the right to privacy.
Data mining is the process of automatically discovering high-level data and trends in large amounts of data that would otherwise remain hidden. The datamining process assumes that all the data is easily accessible at a central location or through centralized access mechanisms such as federated databases and virtual warehouses. However, sometimes the data are distributed among various parties. Privacy in terms of legal and commercial concerns may prevent the parties from directly sharing some sensitive data. Sensitive data usually includes information regarding individuals’ physical or mental health, financial privacy, etc. Privacy advocates and data mining are frequently at odds with each other, and bringing the data together in one place for analysis is not possible due to the privacy laws or policies. How parties collaboratively conduct data mining without breaching data privacy presents a major challenge. The problem is not data mining itself, but the way data mining is done. In this chapter, some techniques for PPDM are introduced.
This chapter is organized as follows. Section 6.1 introduces the issues about privacy and data mining. Section 6.2 discusses the relationship between security, privacy and data mining. Section 6.3 introduces the foundation for PPDM. Section 6.4 discusses the collusion behaviors in PPDM. Concluding remarks are given in the Section 6.5.
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Yin, Y., Kaku, I., Tang, J., Zhu, J. (2011). Privacy-preserving Data Mining. In: Data Mining. Decision Engineering. Springer, London. https://doi.org/10.1007/978-1-84996-338-1_6
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DOI: https://doi.org/10.1007/978-1-84996-338-1_6
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