An Economic Analysis of Privacy Protection and Statistical Accuracy as Social Choices
This work addresses a resource allocation problem for statistical agencies, offering a framework to guide decision-making, though it is incremental as it builds on existing economic and computer science concepts.
The paper tackles the trade-off between privacy protection and statistical accuracy in data publication by proposing an economic model where optimal allocation balances marginal costs and benefits, using differentially private algorithms and U.S. statistical programs as examples.
Statistical agencies face a dual mandate to publish accurate statistics while protecting respondent privacy. Increasing privacy protection requires decreased accuracy. Recognizing this as a resource allocation problem, we propose an economic solution: operate where the marginal cost of increasing privacy equals the marginal benefit. Our model of production, from computer science, assumes data are published using an efficient differentially private algorithm. Optimal choice weighs the demand for accurate statistics against the demand for privacy. Examples from U.S.\ statistical programs show how our framework can guide decision-making. Further progress requires a better understanding of willingness-to-pay for privacy and statistical accuracy.