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Procedia Computer Science
Article . 2014 . Peer-reviewed
License: CC BY NC ND
Data sources: Crossref
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
Procedia Computer Science
Article . 2014
License: CC BY NC ND
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
DBLP
Conference object . 2022
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Efficient Private Information Retrieval for Geographical Aggregation

Authors: Fida Kamal Dankar; Khaled El Emam; Stan Matwin;

Efficient Private Information Retrieval for Geographical Aggregation

Abstract

AbstractKnowledge of patients’ location information (postal/zip codes) is critical in public health research. However, the inclusion of location information makes it easier to determine the identity of the individuals in the data sets. An efficient way to anonymize location information is through aggregation. In order to aggregate the locations efficiently, the data holder needs to know the locations’ adjacency information. A location adjacency matrix is big, and requires constant updates, thus it cannot be stored at the data holder's end. A possible solution would be to have the adjacency matrix stored on a cloud server, the data holder can then query the required adjacency records. However, queries reveal information on patients’ locations, thus, we need to privately query the cloud server's database. Existing private information retrieval protocols are inefficient for our context, therefore, in this paper, we present an efficient protocol to privately query the server's database for adjacency information and thus preserving patients’ privacy.

Keywords

private information retrieval, k-anonymity., privacy

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
gold