A Multi-Party Protocol for Privacy-Preserving Range Queries View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2014

AUTHORS

Maryam Sepehri , Stelvio Cimato , Ernesto Damiani

ABSTRACT

Privacy-preserving query processing (PPQP) techniques are increasingly important in collaborative scenarios, where users need to execute queries on large amount of data shared among different parties who do not want to disclose private data to the others. In many cases, secure multi-party computation (SMC) protocols can be applied, but the resulting solutions are known to suffer from high computation and communication costs. In this paper, we describe a scalable protocol for performing queries in distributed data while respecting the data owners’ privacy. Our solution is applicable both to equality and range queries, and relies on a bucketization technique in order to reduce time complexity. We show the effectiveness of our approach through theoretical and practical analysis. More... »

PAGES

108-120

Book

TITLE

Secure Data Management

ISBN

978-3-319-06810-7
978-3-319-06811-4

Author Affiliations

From Grant

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-06811-4_15

DOI

http://dx.doi.org/10.1007/978-3-319-06811-4_15

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1025423768


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