Title: PPPDM - a privacy-preserving platform for data mashup
Authors: Mahmoud Barhamgi; Djamal Benslimane; Chirine Ghedira; Aïcha-Nabila Benharkat; Alda Lopes Gancarski
Addresses: LIRIS Laboratory, Claude Bernard Lyon 1 University, 69622 Villeurbanne, France. ' LIRIS Laboratory, Claude Bernard Lyon 1 University, 69622 Villeurbanne, France. ' IAE, Universite Jean Moulin Lyon 3, 6, cours Albert Thomas - BP 8242 69355, Lyon Cedex 08, France. ' LIRIS Laboratory, Université de Lyon, CNRS, INSA-Lyon, LIRIS, UMR5205, F-69621, France. ' TELECOM SudParis, 9 rue Charles Fourier, 91011 Evry Cedex, France
Abstract: Data mashup is an important class of the situational applications that combines information on the fly from multiple data sources to respond to immediate business data needs. Mashing-up data requires important programming skills on the side of mashups' creators, and involves handling many challenging privacy and security concerns raised by data providers. In this paper, we propose a declarative approach for mashing-up data. The approach allows the mashups' creators to create data mashups without any programming involved, they just need to specify 'declaratively' their data needs. The approach exploits the mature query rewriting techniques to build the mashups automatically while taking into account the data's privacy and security concerns. We apply the proposed approach to the healthcare domain, and report a thorough experimental evaluation. The reported results are very promising.
Keywords: privacy preservation; privacy protection; data mashup; DaaS web services; query rewriting; security; healthcare domain; declarative approach.
International Journal of Grid and Utility Computing, 2012 Vol.3 No.2/3, pp.175 - 187
Received: 21 Jun 2011
Accepted: 18 Nov 2011
Published online: 20 Dec 2014 *