TY - JOUR
T1 - Efficient private information retrieval for geographical aggregation
AU - Dankar, Fida K.
AU - El Emam, Khaled
AU - Matwin, Stan
N1 - Funding Information:
The authors acknowledge the support for their work provided by the Natural Sciences and Engineering Research Council of Canada through the Strategic Grant Program, as well as the support by Sidra Medical and Research Center, and by IBM Canada through the Southern Ontario Smart Computing Innovation Program (SOSCIP).
Publisher Copyright:
© 2014 The Authors.
PY - 2014
Y1 - 2014
N2 - Knowledge 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.
AB - Knowledge 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.
KW - K-anonymity
KW - Privacy
KW - Private information retrieval
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U2 - 10.1016/j.procs.2014.08.074
DO - 10.1016/j.procs.2014.08.074
M3 - Conference article
AN - SCOPUS:84930363178
VL - 37
SP - 497
EP - 502
JO - Procedia Computer Science
JF - Procedia Computer Science
SN - 1877-0509
T2 - 5th International Conference on Emerging Ubiquitous Systems and Pervasive Networks, EUSPN 2014 and the 4th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare, ICTH 2014
Y2 - 22 September 2014 through 25 September 2014
ER -