Privacy-Preserving Stacking with Application to Cross-organizational Diabetes Prediction
Authors: Quanming Yao, Xiawei Guo, James Kwok, Weiwei Tu, Yuqiang Chen, Wenyuan Dai, Qiang Yang
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we not only demonstrate the effectiveness of our method on two benchmark data sets, i.e., MNIST and NEWS20, but also apply it into a real application of cross-organizational diabetes prediction from RUIJIN data set, where privacy is of a significant concern. |
| Researcher Affiliation | Collaboration | 14Paradigm Inc 2Department of Computer Science and Engineering, HKUST |
| Pseudocode | Yes | Algorithm 1 PLR: Privacy-preserving logistic regression. Algorithm 2 PST-S: Privacy-preserving stacking with SP. Algorithm 3 PST-F: Privacy-preserving stacking with FP. Algorithm 4 PST-H: Privacy-preserving stacking with HTL. |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | Experiments are performed on two popular benchmark data sets for evaluating privacy-preserving learning algorithms [Shokri and Shmatikov, 2015; Papernot et al., 2017; Wang et al., 2018]: MNIST [Le Cun et al., 1998] and NEWS20 [Lang, 1995] (Table 1). |
| Dataset Splits | Yes | 60% of them are used for training (with 1/3 of this used for validation), and the remaining 20% for testing. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependency details, such as library names with version numbers, required to replicate the experiments. |
| Experiment Setup | Yes | We use K = 5 and 50% of the data for Dl and the remaining for Dh. We set ϵsrc = ϵtgt = 1.0. Hyper-parameters are tuned using the validation set. |