Privacy-aware Synthesizing for Crowdsourced Data
Authors: Mengdi Huai, Di Wang, Chenglin Miao, Jinhui Xu, Aidong Zhang
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | 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 Buffalo, NY, USA |
| Pseudocode | Yes | Algorithm 1 Private test-based synthetics release for 표푖 |
| 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 훾and 푘are set as 4 and 5 respectively. |