Information Design for Differential Privacy
| Autor: | Nathan Yoder, Ian M. Schmutte |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Value (ethics) History Computer Science - Cryptography and Security Polymers and Plastics business.industry End user Computer science Decision problem Information design Computer security computer.software_genre Industrial and Manufacturing Engineering FOS: Economics and business Face (geometry) Economics - Theoretical Economics Theoretical Economics (econ.TH) Differential privacy Aggregate data Business and International Management business Cryptography and Security (cs.CR) Publication computer |
| Zdroj: | SSRN Electronic Journal. |
| ISSN: | 1556-5068 |
| Popis: | Firms and statistical agencies must protect the privacy of the individuals whose data they collect, analyze, and publish. Increasingly, these organizations do so by using publication mechanisms that satisfy differential privacy. We consider the problem of choosing such a mechanism so as to maximize the value of its output to end users. We show that this is a constrained information design problem, and characterize its solution. When the underlying database is drawn from a symmetric distribution -- for instance, if individuals' data are i.i.d. -- we show that the problem's dimensionality can be reduced, and that its solution belongs to a simpler class of mechanisms. When, in addition, data users have supermodular payoffs, we show that the simple geometric mechanism is always optimal by using a novel comparative static that ranks information structures according to their usefulness in supermodular decision problems. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|