Learning privately from multiparty data
Authors: Jihun Hamm, Yingjun Cao, Mikhail Belkin
ICML 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the performance of our method with realistic tasks of activity recognition, network intrusion detection, and malicious URL detection. and We evaluate the methods with real tasks in Sec. 6 and conclude the paper in Sec. 7. |
| Researcher Affiliation | Academia | Jihun Hamm HAMMJ@CSE.OHIO-STATE.EDU Dept. Computer Science and Engineering, Ohio State University, Columbus, OH 43210, USA and Paul Cao YIC242@ENG.UCSD.EDU Dept. Computer Science and Engineering, UC-San Diego, La Jolla, CA 92093, USA and Mikhail Belkin MBELKIN@CSE.OHIO-STATE.EDU Dept. Computer Science and Engineering, Ohio State University, Columbus, OH 43210, USA |
| Pseudocode | Yes | Algorithm 1 DP Ensemble by Majority-voted ERM and Algorithm 2 DP Ensemble by Weighted ERM |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the methodology described. |
| Open Datasets | Yes | We use the UCI Human Activity Recognition Dataset (Anguita et al., 2012), which is a collection of motion sensor data on a smart device by multiple subjects performing 6 activities... and For this task we use the KDD-99 dataset, which consists of examples of bad connections... and The Malicious URL Dataset (Ma et al., 2009) is a collection of examples of malicious URLs... |
| Dataset Splits | No | The paper specifies training and testing sample sizes (e.g., 'The training and testing samples are 7K and 3K, respectively.' for Activity Recognition) but does not explicitly mention a separate validation split or how model selection was performed using validation data. |
| Hardware Specification | No | The paper does not provide 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 dependencies with version numbers (e.g., library names, programming language versions, or specific solver versions) used in the experiments. |
| Experiment Setup | Yes | The only hyperparameter of the model is the regularization coefficient λ which we fixed to 10 4 after performing some preliminary experiments. |