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http://dx.doi.org/10.9728/dcs.2017.18.8.1671

Development of Simulation Tool to Support Privacy-Preserving Data Collection  

Kim, Dae-Ho (Department of Computer Science, Sangmyung University)
Kim, Jong Wook (Department of Computer Science, Sangmyung University)
Publication Information
Journal of Digital Contents Society / v.18, no.8, 2017 , pp. 1671-1676 More about this Journal
Abstract
In theses days, data has been explosively generated in diverse industrial areas. Accordingly, many industries want to collect and analyze these data to improve their products or services. However, collecting user data can lead to significant personal information leakage. Local differential privacy (LDP) proposed by Google is the state-of-the-art approach that is used to protect individual privacy in the process of data collection. LDP guarantees that the privacy of the user is protected by perturbing the original data at the user's side, but a data collector is still able to obtain population statistics from collected user data. However, the prevention of leakage of personal information through such data perturbation mechanism may cause the significant reduction in the data utilization. Therefore, the degree of data perturbation in LDP should be set properly depending on the data collection and analysis purposes. Thus, in this paper, we develop the simulation tool which aims to help the data collector to properly chose the degree of data perturbation in LDP by providing her/him visualized simulated results with various parameter configurations.
Keywords
Differential Privacy; Local Differential Privacy; Data Visualization;
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Times Cited By KSCI : 1  (Citation Analysis)
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