k-anonymity-based horizontal fragmentation to preserve privacy in data outsourcing

Authors:
Abbas Taheri Soodejani;Mohammad Ali Hadavi;Rasool Jalili
Affiliations:
Data and Network Security Laboratory, Department of Computer Engineering, Sharif University of Technology, Iran;Data and Network Security Laboratory, Department of Computer Engineering, Sharif University of Technology, Iran;Data and Network Security Laboratory, Department of Computer Engineering, Sharif University of Technology, Iran
Venue:
DBSec'12 Proceedings of the 26th Annual IFIP WG 11.3 conference on Data and Applications Security and Privacy
Year:
2012

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Preserving Privacy in Data Outsourcing

Preserving Privacy in Data Outsourcing

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Abstract

This paper proposes a horizontal fragmentation method to preserve privacy in data outsourcing. The basic idea is to identify sensitive tuples, anonymize them based on a privacy model and store them at the external server. The remaining non-sensitive tuples are also stored at the server side. While our method departs from using encryption, it outsources all the data to the server; the two important goals that existing methods are unable to achieve simultaneously. The main application of the method is for scenarios where encrypting or not outsourcing sensitive data may not guarantee the privacy.