[Paper Review] A Game-Theoretic Study on Non-Monetary Incentives in Data Analytics Projects with Privacy Implications

Michela Chessa, Jens Großklags+1 more|arXiv (Cornell University)|May 10, 2015

Privacy-Preserving Technologies in Data43 references4 citations

TL;DR

This paper proposes a game-theoretic model where individuals voluntarily contribute data at self-chosen precision levels to a data analytics project, treating the resulting population estimate as a public good. By imposing a minimum precision requirement, the analyst significantly improves estimation accuracy without monetary incentives, achieving higher-quality public goods through strategic restriction of user choice, even in heterogeneous populations with varying privacy preferences.

ABSTRACT

The amount of personal information contributed by individuals to digital repositories such as social network sites has grown substantially. The existence of this data offers unprecedented opportunities for data analytics research in various domains of societal importance including medicine and public policy. The results of these analyses can be considered a public good which benefits data contributors as well as individuals who are not making their data available. At the same time, the release of personal information carries perceived and actual privacy risks to the contributors. Our research addresses this problem area. In our work, we study a game-theoretic model in which individuals take control over participation in data analytics projects in two ways: 1) individuals can contribute data at a self-chosen level of precision, and 2) individuals can decide whether they want to contribute at all (or not). From the analyst's perspective, we investigate to which degree the research analyst has flexibility to set requirements for data precision, so that individuals are still willing to contribute to the project, and the quality of the estimation improves. We study this tradeoff scenario for populations of homogeneous and heterogeneous individuals, and determine Nash equilibria that reflect the optimal level of participation and precision of contributions. We further prove that the analyst can substantially increase the accuracy of the analysis by imposing a lower bound on the precision of the data that users can reveal.

Motivation & Objective

• To model individual incentives in data analytics projects where participants face a tradeoff between privacy costs and benefits from public good outcomes.
• To analyze how analysts can improve estimation accuracy by setting minimum precision requirements for data contributions.
• To understand the impact of individual heterogeneity in privacy preferences on participation and data quality.
• To investigate whether strategic constraints on user choice can enhance public good provision in non-monetary incentive settings.
• To extend the model to costly data acquisition and multi-dimensional estimation scenarios.

Proposed method

• Formalizes a non-cooperative game where individuals choose both whether to contribute and at what precision level.
• Models the analyst’s goal as estimating the population average of a scalar quantity using contributions from n individuals.
• Introduces a minimum precision constraint as a mechanism to restrict strategy space and improve estimation quality.
• Analyzes Nash equilibria under homogeneous and heterogeneous agent assumptions, proving uniqueness of equilibrium strategies.
• Considers two-stage decision structures where agents learn contribution levels before choosing precision, and evaluates information's impact on accuracy.
• Extends the model to cases with non-negligible data collection costs, deriving optimal sampling strategies for homogeneous and heterogeneous populations.

Experimental results

Research questions

• RQ1 To what extent can an analyst improve estimation accuracy by imposing a minimum precision requirement on data contributors?
• RQ2 How do heterogeneous privacy preferences affect individual participation and data precision in non-monetary data analytics projects?
• RQ3 Does providing information about prior contributors' choices improve the accuracy of population estimates in such games?
• RQ4 What is the optimal number of users to solicit when data collection incurs a cost, and how does this depend on privacy cost functions?
• RQ5 Can the proposed mechanism be generalized to multi-dimensional or model-based estimation tasks?

Key findings

• Imposing a minimum precision level on data contributions significantly increases the accuracy of the population estimate, even without monetary incentives.
• A unique Nash equilibrium exists in all considered cases, including homogeneous and heterogeneous populations, ensuring strategic predictability.
• Providing information about prior contributors' choices does not improve estimation accuracy, contrary to intuitive expectations.
• The optimal strategy for the analyst under costly data collection involves selecting a subset of users based on their privacy cost functions, with a clear ordering derived from Theorem 4.
• The method remains robust under arbitrary privacy cost and estimation cost functions, satisfying only mild assumptions, enhancing its generalizability.
• The approach offers a simple, non-monetary alternative to monetary compensation for improving public good provision in data analytics.