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

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  • Rajeev Kumar

    (Department of Business Administration, College of Business, Kutztown University, Kutztown, PA, USA)

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.

Suggested Citation

  • Rajeev Kumar, 2017. "Algorithms for Selecting the Optimum Dataset While Providing Personalized Privacy and Compensation to its Participants," International Journal of Operations Research and Information Systems (IJORIS), IGI Global, vol. 8(4), pages 43-58, October.
    • Handle: RePEc:igg:joris0:v:8:y:2017:i:4:p:43-58
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