Differentially private count queries over personalized-location trajectory databases
| Autor: | Mahdi Abadi, Fatemeh Deldar |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
021110 strategic
defence & security studies Measure (data warehouse) Multidisciplinary Database Computer science Privacy protection 0211 other engineering and technologies 020206 networking & telecommunications 02 engineering and technology computer.software_genre lcsh:Computer applications to medicine. Medical informatics Approximation error Trajectory database Individual data 0202 electrical engineering electronic engineering information engineering Trajectory Differential privacy lcsh:R858-859.7 Research article lcsh:Science (General) computer lcsh:Q1-390 |
| Zdroj: | Data in Brief, Vol 20, Iss, Pp 1510-1514 (2018) |
| ISSN: | 2352-3409 |
| Popis: | 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. Keywords: Differential privacy, Count query, Personalized-location trajectory dataset, Benchmark dataset |
| Databáze: | OpenAIRE |
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