Differentially Private Partial Set Cover with Applications to Facility Location
Authors: George Z. Li, Dung Nguyen, Anil Vullikanti
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | For our experiments in our main paper, we focus on the vaccine distribution problem we studied in Section 3. We run all of our experiments on synthetic mobility data from Charlottesville city and Albemarle county, which are part of a synthetic U.S. population (see [Chen et al., 2021; Machi et al., 2021] for details). |
| Researcher Affiliation | Academia | George Z. Li1, Dung Nguyen2, and Anil Vullikanti2 1University of Maryland 2Biocomplexity Institute and Initiative, and Department of Computer Science, University of Virginia gzli929@gmail.com, dungn@virginia.edu, vsakumar@virginia.edu |
| Pseudocode | Yes | Algorithm 1 MP artial Set Cover(U, S, ρ, ϵ, δ) ; Algorithm 2 MMax Coverage(U, S, ρ, ϵ, δ) ; Algorithm 3 DPCLIENTCOVER: |
| Open Source Code | No | The paper provides a link to an arXiv preprint (https://arxiv.org/abs/2207.10240) but does not explicitly state that source code for the methodology is available or provide a link to a code repository. |
| Open Datasets | Yes | We run all of our experiments on synthetic mobility data from Charlottesville city and Albemarle county, which are part of a synthetic U.S. population (see [Chen et al., 2021; Machi et al., 2021] for details). |
| Dataset Splits | No | The paper mentions using "synthetic mobility data" and running "experiments on synthetic mobility data" but does not provide specific details on how this data was split into training, validation, or test sets. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU models, CPU types, or memory) used to conduct the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies (e.g., libraries, frameworks, or solvers) with version numbers that would be required to reproduce the experiments. |
| Experiment Setup | Yes | We set δ = 10 6 and run our private vaccine distribution algorithm for different budgets k {4, . . . , 16} and different privacy parameters ϵ {0.25, 0.5, 1, 2, 4}. A careful reader may notice that Theorem 2.4 only applies for ϵ (0, 1), and may worry that our experimental regime doesn t have privacy guarantees. However, DPCLIENTCOVER makes calls to the Partial Set Cover algorithm with parameter ϵ = ϵ/ log2(1/γ), so we still have differential privacy. |