Differential Privacy for Stackelberg Games

February 01, 2020 Β· Declared Dead Β· πŸ› International Joint Conference on Artificial Intelligence

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Authors Ferdinando Fioretto, Lesia Mitridati, Pascal Van Hentenryck arXiv ID 2002.00944 Category math.OC: Optimization & Control Cross-listed cs.CR, cs.GT Citations 20 Venue International Joint Conference on Artificial Intelligence Last Checked 3 months ago
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 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 approach.
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