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Privacy-Preserving Data Mining from Outsourced Databases

Abstract

Spurred by developments such as cloud computing, there has been considerable recent interest in the paradigm of data mining-as-service: a company (data owner) lacking in expertise or computational resources can outsource its mining needs to a third party service provider (server). However, both the outsourced database and the knowledge extract from it by data mining are considered private property of the data owner. To protect corporate privacy, the data owner transforms its data and ships it to the server, sends mining queries to the server, and recovers the true patterns from the extracted patterns received from the server. In this paper, we study the problem of outsourcing a data mining task within a corporate privacy-preserving framework. We propose a scheme for privacy-preserving outsourced mining which offers a formal protection against information disclosure, and show that the data owner can recover the correct data mining results efficiently.

Keywords

  • Encryption Scheme
  • Frequent Pattern
  • Hash Table
  • Pattern Mining
  • Association Rule Mining

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Correspondence to Fosca Giannotti .

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Giannotti, F., Lakshmanan, L.V., Monreale, A., Pedreschi, D., Wang, H.(. (2011). Privacy-Preserving Data Mining from Outsourced Databases. In: Gutwirth, S., Poullet, Y., De Hert, P., Leenes, R. (eds) Computers, Privacy and Data Protection: an Element of Choice. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-0641-5_19

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