k-Anonymity-Based Horizontal Fragmentation to Preserve Privacy in Data Outsourcing

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.
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Nora Cuppens-Boulahia; Frédéric Cuppens; Joaquin Garcia-Alfaro. 26th Conference on Data and Applications Security and Privacy (DBSec), Jul 2012, Paris, France. Springer, Lecture Notes in Computer Science, LNCS-7371, pp.263-273, 2012, Data and Applications Security and Privacy XXVI. 〈10.1007/978-3-642-31540-4_20〉
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Abbas Soodejani, Mohammad Hadavi, Rasool Jalili. k-Anonymity-Based Horizontal Fragmentation to Preserve Privacy in Data Outsourcing. Nora Cuppens-Boulahia; Frédéric Cuppens; Joaquin Garcia-Alfaro. 26th Conference on Data and Applications Security and Privacy (DBSec), Jul 2012, Paris, France. Springer, Lecture Notes in Computer Science, LNCS-7371, pp.263-273, 2012, Data and Applications Security and Privacy XXVI. 〈10.1007/978-3-642-31540-4_20〉. 〈hal-01534772〉

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