Algorithms for Selecting the Optimum Dataset While Providing Personalized Privacy and Compensation to its Participants

Rajeev Kumar
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Rajeev Kumar: Department of Business Administration, College of Business, Kutztown University, Kutztown, PA, USA

International Journal of Operations Research and Information Systems (IJORIS), 2017, vol. 8, issue 4, 43-58

Abstract: The privacy preserving microdata sharing literature has proposed several techniques that allow a database administrator to share a dataset in a privacy preserving manner. This paper considers the implications of adding a market layer to that setting. In this setting, individuals (data providers) can receive a market-determined compensation in exchange for their information while they also receive a personalized privacy protection. The computational burdens of satisfying a variety of privacy requirements of individuals (sellers) and dataset requirements of the data receiver (buyer) are analyzed in this paper. The author presents a polynomial time reformulation procedure that proves that the “optimum information product” creation problem reduces to multiple-choice knapsack problem, which is a weakly NP hard problem. The problem of various instance sizes is solved using FICO Xpress 7.0 optimization software. The insights presented in the paper can be utilized for creating a market of individual information in different settings.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:igg:joris0:v:8:y:2017:i:4:p:43-58

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