This is not the latest version of this item. The latest version can be found at: https://ecommons.cornell.edu/handle/1813/39081
Revisiting the Economics of Privacy: Population Statistics and Confidentiality Protection as Public Goods
Abowd, John; Schmutte, Ian
We consider the problem of the public release of statistical information about a population–explicitly accounting for the public-good properties of both data accuracy and privacy loss. We first consider the implications of adding the public-good component to recently published models of private data publication under differential privacy guarantees using a Vickery-Clark-Groves mechanism and a Lindahl mechanism. We show that data quality will be inefficiently under-supplied. Next, we develop a standard social planner’s problem using the technology set implied by (ε, δ)-differential privacy with (α, β)-accuracy for the Private Multiplicative Weights query release mechanism to study the properties of optimal provision of data accuracy and privacy loss when both are public goods. Using the production possibilities frontier implied by this technology, explicitly parameterized interdependent preferences, and the social welfare function, we display properties of the solution to the social planner’s problem. Our results directly quantify the optimal choice of data accuracy and privacy loss as functions of the technology and preference parameters. Some of these properties can be quantified using population statistics on marginal preferences and correlations between income, data accuracy preferences, and privacy loss preferences that are available from survey data. Our results show that government data custodians should publish more accurate statistics with weaker privacy guarantees than would occur with purely private data publishing. Our statistical results using the General Social Survey and the Cornell National Social Survey indicate that the welfare losses from under-providing data accuracy while over-providing privacy protection can be substantial.
Alternate location for replication archive: https://doi.org/10.5281/zenodo.290231
Abowd acknowledges direct support from the U.S. Census Bureau and from NSF Grants BCS-0941226, TC-1012593 and SES-1131848.
Demand for public statistics; Technology for statistical agencies; Optimal data accuracy; Optimal confidentiality protection