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 embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Privacy-aware Synthesizing for Crowdsourced Data
Authors: Mengdi Huai, Di Wang, Chenglin Miao, Jinhui Xu, Aidong Zhang
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Both theoretical analysis and extensive experiments on real-world datasets demonstrate the desired performance of the proposed method. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of Virginia, VA, USA 2Department of Computer Science and Engineering, SUNY at Bu๏ฌalo, NY, USA |
| Pseudocode | Yes | Algorithm 1 Private test-based synthetics release for ํํ |
| Open Source Code | No | The paper does not provide any specific links or statements about the availability of open-source code for the described methodology. |
| Open Datasets | Yes | Datasets. We adopt the following real-world datasets for our experiments: Population Dataset [Pasternack and Roth, 2010; Wan et al., 2016], Stock Dataset [Li et al., 2012], and Indoor Floorplan Dataset [Li et al., 2014a]. |
| Dataset Splits | No | The paper mentions using real-world datasets but does not provide specific details on how these datasets were split into training, validation, or test sets. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependency details with version numbers, such as programming languages, libraries, or frameworks used for implementation. |
| Experiment Setup | Yes | Here we assume that the data collector releases 30 synthetic claims for each object to the public. The parameters ํพand ํare set as 4 and 5 respectively. |