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We present a mechanism for computing asymptotically stable school optimalmatchings, while guaranteeing that it is an asymptotic dominant strategy forevery student to report their true preferences to the mechanism. Our main toolin this endeavor is differential privacy: we give an algorithm that coordinatesa stable matching using differentially private signals, which lead to ourtruthfulness guarantee. This is the first setting in which it is known how toachieve nontrivial truthfulness guarantees for students when computing schooloptimal matchings, assuming worst- case preferences (for schools and students)in large markets.

Approximately Stable, School Optimal, and Student-Truthful Many-to-One Matchings (via Differential Privacy)