Differential Privacy for Stackelberg Games
Authors: Ferdinando Fioretto, Lesia Mitridati, Pascal Van Hentenryck
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 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. (...) The theoretical guarantees ensure differential privacy and near optimality, while the experimental results validate the approach on a real test case for the coordination of electricity and natural gas markets in the Northeastern United States [Byeon and Van Hentenryck, 2019]. (...) 7 Experimental Evaluation The performance of the proposed PPSM is illustrated on the motivation problem introduced in Section 2. |
| Researcher Affiliation | Academia | Ferdinando Fioretto1 , Lesia Mitridati2 and Pascal Van Hentenryck2 1Syracuse University 2Georgia Institute of Technology |
| Pseudocode | No | The paper describes the PPSM mechanism using numbered steps ([1], [2a], etc.) in prose, but it does not provide formal pseudocode or an algorithm block. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | The PPSM is evaluated on a test system representing the joint natural gas and electricity systems in the Northeastern US [Byeon and Van Hentenryck, 2019]. |
| Dataset Splits | No | The paper evaluates on a 'test system' and generates 'repetitions for each test case', but it does not specify explicit train/validation/test dataset splits (e.g., percentages or counts) or reference standard predefined splits for reproducibility. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments (e.g., CPU, GPU models, or memory specifications). |
| Software Dependencies | No | The paper mentions 'CPLEX' as a solver but does not provide a version number for it or for any other software dependencies. 'Using the equivalent Karush-Kuhn-Tucker (KKT) conditions of the linear lower-level problem (5d) and the Fortuny-Amat linearization, this bilevel problem can be recast as a mixed-integer second-order cone program (MISOCP) [Gabriel et al., 2012]. The resolution of the privacy-preserving demand profiles (phases [1] and [2] of PPSM) takes less than 30s for any of the instances.' (Implicitly, a solver for MISOCP like CPLEX might be used but no version is given). |
| Experiment Setup | Yes | The electricity demand profile is uniformly increased by a stress factor ranging from 30% to 60%, and the gas demand profile is increased by a stress factor ranging from 10% to 130%, producing increasingly stressed and difficult operating conditions. (...) The experiments compare the proposed PPSM to a version (PPSMp) that omits the fidelity constraint on the dual variables (5c). Both versions are compared with the standard Laplace mechanism for varying values of the privacy parameter α P t0.1, 1, 10u ˆ 102 MWh, and the fidelity parameters ηp ηd P t0.01, 0.1, 10.0u% of the original objective value of the GM p Og q and gas prices pyg q, respectively. |