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Differentially private count queries over personalized-location trajectory databases.

ABSTRACT: Differential privacy is a technique for releasing statistical information about a database without revealing information about its individual data records. Also, a personalized-location trajectory database is a trajectory database where locations have different privacy protection requirements and, thus, are privacy conscious. This data article is related to the research article entitled "PLDP-TD: Personalized-location differentially private data analysis on trajectory databases" (Deldar and Abadi, 2018 [1]), in which we introduced a new differential privacy notion for personalized-location trajectory databases, and devised a novel differentially private algorithm, called PLDP-TD, to implement this new privacy notion. Here, we describe how the datasets in the research article were obtained and measure the relative error of PLDP-TD for different non-zero count query sets.

SUBMITTER: Deldar F

PROVIDER: S-EPMC6153358 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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Differentially private count queries over personalized-location trajectory databases.

Deldar Fatemeh F Abadi Mahdi M

Data in brief 20180903

Differential privacy is a technique for releasing statistical information about a database without revealing information about its individual data records. Also, a personalized-location trajectory database is a trajectory database where locations have different privacy protection requirements and, thus, are privacy conscious. This data article is related to the research article entitled "PLDP-TD: Personalized-location differentially private data analysis on trajectory databases" (Deldar and Abad ...[more]

PMID: 30258955

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