| Paper ID | IFS-6.4 |
| Paper Title |
SCALABLE PRIVACY-PRESERVING DISTRIBUTED EXTREMELY RANDOMIZED TREES FOR STRUCTURED DATA WITH MULTIPLE COLLUDING PARTIES |
| Authors |
Amin Aminifar, Western Norway University of Applied Sciences, Norway; Fazle Rabbi, University of Bergen, Western Norway University of Applied Sciences, Norway; Yngve Lamo, Western Norway University of Applied Sciences, Norway |
| Session | IFS-6: Anonymization, Security and Privacy |
| Location | Gather.Town |
| Session Time: | Thursday, 10 June, 15:30 - 16:15 |
| Presentation Time: | Thursday, 10 June, 15:30 - 16:15 |
| Presentation |
Poster
|
| Topic |
Information Forensics and Security: [ADP] Anonymization And Data Privacy |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Today, in many real-world applications of machine learning algorithms, the data is stored on multiple sources instead of at one central repository. In many such scenarios, due to privacy concerns and legal obligations, e.g., for medical data, and communication/computation overhead, for instance for large scale data, the raw data cannot be transferred to a center for analysis. Therefore, new machine learning approaches are proposed for learning from the distributed data in such settings. In this paper, we extend the distributed Extremely Randomized Trees (ERT) approach w.r.t. privacy and scalability. First, we extend distributed ERT to be resilient w.r.t. the number of colluding parties in a scalable fashion. Then, we extend the distributed ERT to improve its scalability without any major loss in classification performance. We refer to our proposed approach as k-PPD-ERT or Privacy-Preserving Distributed Extremely Randomized Trees with $k$ colluding parties. |
