{"type":"ScholarlyArticle","title":"Differential Privacy for Stackelberg Games","authors":[{"name":"Ferdinando Fioretto","slug":"ferdinando-fioretto","given":"Ferdinando","family":"Fioretto","affiliation":"Syracuse University"},{"name":"Lesia Mitridati","slug":"lesia-mitridati","given":"Lesia","family":"Mitridati","affiliation":"Georgia Institute of Technology"},{"name":"Pascal Van Hentenryck","slug":"pascal-van-hentenryck","given":"Pascal","family":"Van Hentenryck","affiliation":"Georgia Institute of Technology"}],"abstract":"This paper introduces a differentially private (DP) mechanism to protect the information exchanged during the coordination of sequential and interdependent markets. This coordination represents a classic Stackelberg game and relies on the exchange of sensitive information between the system agents. The paper is motivated by the observation that the perturbation introduced by traditional DP mechanisms fundamentally changes the underlying optimization problem and even leads to unsatisfiable instances. To remedy such limitation, the paper introduces the Privacy-Preserving Stackelberg Mechanism (PPSM), a framework that enforces the notions of feasibility and fidelity (i.e. near-optimality) of the privacy-preserving information to the original problem objective. PPSM complies with the notion of differential privacy and ensures that the outcomes of the privacy-preserving coordination mechanism are close-to-optimality for each agent. Experimental results on several gas and electricity market benchmarks based on a real case study demonstrate the effectiveness of the proposed approach. A full version of this paper [Fioretto et al., 2020b] contains complete proofs and additional discussion on the motivating application.","doi":"10.24963/ijcai.2020/481","url":"https://ijcai.org/proceedings/2020/481","pdf":"https://ijcai.org/proceedings/2020/0481.pdf","markdown":"https://ijcai.org/proceedings/2020/481.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/481","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Multidisciplinary Topics and Applications","track":"Main track","number":481,"pages":{"first":3480,"last":3486},"keywords":["Agent-based and Multi-agent Systems: Noncooperative Games","Multidisciplinary Topics and Applications: Security and Privacy","Constraints and SAT: Constraint Optimization"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24574/file_24574.m3u8","length_sec":253,"provider":"hls"}],"references":[{"position":1,"raw":"[Allen et al., 2008] Eric H Allen, Jeffrey H Lang, and Mar- ija D Ilic. 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