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The Privacy Paradox and Optimal Bias–Variance Trade-offs in Data Acquisition

Author

Listed:
  • Guocheng Liao

    (School of Software Engineering, Sun Yat-sen University, Zhuhai 519082, China)

  • Yu Su

    (Computing and Mathematical Sciences Department, California Institute of Technology, Pasadena, California 91125)

  • Juba Ziani

    (Industrial and Systems Engineering Department, Georgia Institute of Technology, Atlanta, Georgia 30332)

  • Adam Wierman

    (Computing and Mathematical Sciences Department, California Institute of Technology, Pasadena, California 91125)

  • Jianwei Huang

    (School of Science and Engineering, Shenzhen Institute of Artificial Intelligence and Robotics for Society, The Chinese University of Hong Kong, Shenzhen, Shenzhen 518172, China)

Abstract

Whereas users claim to be concerned about privacy, often they do little to protect their privacy in their online actions. One prominent explanation for this privacy paradox is that, when an individual shares data, it is not just the individual’s privacy that is compromised; the privacy of other individuals with correlated data is also compromised. This information leakage encourages oversharing of data and significantly impacts the incentives of individuals in online platforms. In this paper, we study the design of mechanisms for data acquisition in settings with information leakage and verifiable data. We design an incentive-compatible mechanism that optimizes the worst case trade-off between bias and variance of the estimation subject to a budget constraint, with which the worst case is over the unknown correlation between costs and data. Additionally, we characterize the structure of the optimal mechanism in closed form and study monotonicity and nonmonotonicity properties of the marketplace.

Suggested Citation

  • Guocheng Liao & Yu Su & Juba Ziani & Adam Wierman & Jianwei Huang, 2024. "The Privacy Paradox and Optimal Bias–Variance Trade-offs in Data Acquisition," Mathematics of Operations Research, INFORMS, vol. 49(4), pages 2749-2767, November.
    • Handle: RePEc:inm:ormoor:v:49:y:2024:i:4:p:2749-2767
    DOI: 10.1287/moor.2023.0022
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