Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Security Games with Information Leakage: Modeling and Computation
Authors: Haifeng Xu, Albert Xing Jiang, Arunesh Sinha, Zinovi Rabinovich, Shaddin Dughmi, Milind Tambe
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experiments Traditional algorithms for computing Strong Stackelberg Equilibrium (SSE) only optimize the coverage probability at each target, without considering their correlations. In this section, we experimentally study how traditional algorithms and our new algorithms perform in presence of probabilistic or adversarial information leakage. ... All algorithms are tested on the following two sets of data: Los Angeles International Airport (LAX) Checkpoint Data ... Simulated Game Payoffs. |
| Researcher Affiliation | Academia | Haifeng Xu Albert X. Jiang Arunesh Sinha USC Trinity University USC EMAIL EMAIL EMAIL Zinovi Rabinovich Shaddin Dughmi Milind Tambe Independent Researcher USC USC EMAIL EMAIL EMAIL |
| Pseudocode | Yes | Algorithm 1 Defender Oracle Input: matrix A of form (4). Output: a pure strategy s. ... Algorithm 2 Max-Entropy Sampling Input: : α [0, )n, k. Output: : a pure strategy s with |s| = k. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the described methodology. |
| Open Datasets | Yes | Los Angeles International Airport (LAX) Checkpoint Data from [Pita et al., 2008]. |
| Dataset Splits | No | The paper does not explicitly provide training, validation, or test dataset splits with percentages or sample counts. |
| Hardware Specification | No | The paper states 'All algorithms run efficiently as expected (terminate within seconds using MATLAB)' but does not provide specific hardware details like CPU/GPU models, memory, or specific computing environments. |
| Software Dependencies | No | The paper mentions 'using MATLAB' but does not provide specific version numbers for MATLAB or any other software dependencies, libraries, or solvers. |
| Experiment Setup | Yes | All generated games have 20 targets and 10 resources. The reward ri (cost ci) of each target i is chosen uniformly at random from the interval [0, 10] ([ 10, 0]). ... For probabilistic information leakage, we randomly generate the probabilities that each target leaks information with the constraint Pn i=1 pi = 1 p0. For the case of leakage from small support (for simulated payoffs only), we randomly choose a support of size 5. |