2 4 O ct 2 01 4 Approximately Stable , School Optimal , and Student - Truthful Many - to - One Matchings ( via Differential Privacy ) ∗
نویسندگان
چکیده
We present a mechanism for computing asymptotically stable school optimal matchings, while guaranteeing that it is an asymptotic dominant strategy for every student to report their true preferences to the mechanism. Our main tool in this endeavor is differential privacy: we give an algorithm that coordinates a stable matching using differentially private signals, which lead to our truthfulness guarantee. This is the first setting in which it is known how to achieve nontrivial truthfulness guarantees for students when computing school optimal matchings, assuming worstcase preferences (for schools and students) in large markets. Kannan was partially supported by NSF grant NRI-1317788. email: [email protected]. Morgenstern was partially supported by NSF grants CCF-1116892 and CCF-1101215, as well as a Simons Award for Graduate Students in Theoretical Computer Science. Contact information: J. Morgenstern, Computer Science Department, Carnegie Mellon University, [email protected]. Roth was partially supported by an NSF CAREER award, NSF Grants CCF-1101389 and CNS-1065060, and a Google Focused Research Award. Email: [email protected]. Wu was supported in part by NSF Grants CCF-1101389. Email: [email protected]
منابع مشابه
Approximately Stable, School Optimal, and Student-Truthful Many-to-One Matchings (via Differential Privacy)
We present a mechanism for computing asymptotically stable school optimal matchings, while guaranteeing that it is an asymptotic dominant strategy for every student to report their true preferences to the mechanism. Our main tool in this endeavor is differential privacy: we give an algorithm that coordinates a stable matching using differentially private signals, which lead to our truthfulness ...
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