Privacy-Preserving Obfuscation of Critical Infrastructure Networks

Authors: Ferdinando Fioretto, Terrence W.K. Mak, Pascal Van Hentenryck

IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental The obfuscation is evaluated for both realism and privacy properties on real energy and transportation networks. Experimental results show the obfuscation mechanism substantially reduces the potential damage of an attack exploiting the released data to harm the real network.
Researcher Affiliation Academia Ferdinando Fioretto1,2 , Terrence W.K. Mak1 and Pascal Van Hentenryck1 1 Georgia Institute of Technology 2 Syracuse University {fioretto, wmak}@gatech.edu, pvh@isye.gatech.edu
Pseudocode No The paper presents mathematical models (Figure 4: PBL, Figure 5: PCL) but does not include pseudocode or clearly labeled algorithm blocks.
Open Source Code No The paper does not provide an explicit statement about releasing its source code or a link to a code repository.
Open Datasets Yes The experiments were performed on a variety of benchmarks from the NESTA library [Coffrin et al., 2014].
Dataset Splits No The paper does not explicitly specify training, validation, and test dataset splits by percentage or sample count. It refers to 'real energy and transportation networks' and benchmarks, but not the split methodology.
Hardware Specification No The paper does not provide any specific details about the hardware used to run the experiments (e.g., CPU, GPU models, memory, or cloud instances).
Software Dependencies No The paper mentions 'the Julia package Power Models.jl with IPOPT', but does not provide specific version numbers for these software dependencies, which is required for reproducibility.
Experiment Setup Yes The experiments use a privacy loss ϵ of 1.0, and vary the indistinguishability levels αl from 1% to 10% of the network diameter dp Gq, and the faithfulness level β in t10 2, 10 1u, while αv is fixed to 0.1 p.u. 10 MW.