Differential Privacy for Stackelberg Games
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Abstract (arXiv)
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.
Key facts. Cited by 5 papers in the map (data through 13 Oct 2026). First posted on arXiv 1 Feb 2020.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 1; 2024: 2.
5 papers.
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- 2024Heterogeneous Causal Metapath Graph Neural Network for Gene-Microbe-Disease Association Predictioncited by 1
- 2023HeTriNet: Heterogeneous Graph Triplet Attention Network for Drug-Target-Disease Interactioncited by 0
- 2024DrugCLIP: Contrastive Drug-Disease Interaction For Drug Repurposing2 Jul 2024
- 2024Heterogeneous Causal Metapath Graph Neural Network for Gene-Microbe-Disease Association Prediction27 Jun 2024
- 2023HeTriNet: Heterogeneous Graph Triplet Attention Network for Drug-Target-Disease Interaction30 Nov 2023
- 2022JULIA: Joint Multi-linear and Nonlinear Identification for Tensor Completion31 Jan 2022
- 2021Deep Learning Based Reconstruction of Total Solar Irradiance23 Jul 2021