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# Mechanism Design via Differential Privacy

Providence, RI, pp.94-103, (2007)

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Abstract

We study the role that privacy-preserving algorithms, which prevent the leakage of specific information about participants, can play in the design of mechanisms for strategic agents, which must encourage players to honestly report information. Specifically, we show that the recent notion of differential privacy [15, 14], in addition to it...More

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Introduction

- The problem of analyzing sensitive data with an eye towards maintaining its privacy has existed for some time.
- Problems faced by the Census Bureau, among other examples, helped to develop the study of “disclosure limitation mechanisms”1, which aim to limit the amount or nature of specific information that leaks out of a data set.
- Specific guarantees given by different techniques are, naturally, different, and the tendency is to formally characterize privacy as protection from the disclosures prevented by the mechanism at hand, rather than aiming for any specific privacy goal

Highlights

- The problem of analyzing sensitive data with an eye towards maintaining its privacy has existed for some time
- We have seen how Differential Privacy extends beyond disclosure limitation to give broad game theoretic guarantees, including approximate truthfulness, collusion resistance, and repeatable play
- We have introduced a new general mechanism with differential privacy that comes with guarantees about the quality of the output, even for functions that are not robust to additive noise, and those whose output may not even permit perturbation
- This mechanism skews a base measure to the largest degree possible while ensuring differential privacy, focusing probability on the outputs of highest value
- We applied this general mechanism to several auction problems, yielding revenue that is within an additive logarithmic term of optimal

Conclusion

- The authors have seen how Differential Privacy extends beyond disclosure limitation to give broad game theoretic guarantees, including approximate truthfulness, collusion resistance, and repeatable play.
- The authors have introduced a new general mechanism with differential privacy that comes with guarantees about the quality of the output, even for functions that are not robust to additive noise, and those whose output may not even permit perturbation
- This mechanism skews a base measure to the largest degree possible while ensuring differential privacy, focusing probability on the outputs of highest value.
- The authors stress that unlike some previous work, eg [6], the mechanism is not strictly truthful

Reference

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