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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Post-processing of Differentially Private Data: A Fairness Perspective
Authors: Keyu Zhu, Ferdinando Fioretto, Pascal Van Hentenryck
IJCAI 2022 | Venue PDF | LLM Run Details | Input Tokens: 14,210 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,666 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The theoretical analysis is complemented with numerical simulations on Census data. |
| Researcher Affiliation | Academia | 1Georgia Institute of Technology 2Syracuse University |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. There are no repository links or explicit code release statements. |
| Open Datasets | No | The paper mentions using 'US Census data' and '2010 US census release' but does not provide concrete access information (specific link, DOI, repository name, or formal citation with authors/year) for the specific dataset instances used in their simulations, nor does it refer to an established benchmark dataset with explicit access details. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | The experiments use the Laplace mechanism with parameter λ 10 and the Gaussian mechanism with parameter σ 25. The empirical studies of α-fairness and its bounds in Theorem 1 associated with the post-processing mechanism πS over 1, 000, 000 independent runs are reported in Table 1. |