Valuation of Differential Privacy Budget in Data Trade: A Conjoint Analysis

Differential privacy has been proposed as a rigorous privacy guarantee for computation mechanisms. However, it is still unclear how data collectors can correctly and intuitively configure the value of the privacy budget parameter ε for differential privacy, such that the privacy of involved individuals is protected. In this work, we seek to investigate the trade-offs between differential privacy valuation, scenario properties, and preferred differential privacy level of individuals in a data trade. Using a choice-based conjoint analysis (N= 139 ), we mimic the decision-making process of individuals under different data-sharing scenarios. We found that, as hypothesized, individuals required lower payments from a data collector for sharing their data, as more substantial perturbation was applied as part of a differentially private data analysis. Furthermore, respondents selected scenarios with lower ε values (requiring more privacy) for indefinitely-retained data for profit generation than for temporarily-retained data with a non-commercial purpose. Our findings may help data processors better tune the differential privacy budget for their data analysis based on individual privacy valuation and contextual properties.