Print Email Facebook Twitter Optimised Private Set Intersection for Vertical Federated Tree Models Title Optimised Private Set Intersection for Vertical Federated Tree Models Author Li, Martin (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Hai, R. (mentor) Zhan, D. (mentor) Lofi, C. (mentor) Decouchant, Jérémie (graduation committee) Degree granting institution Delft University of Technology Programme Computer Science Date 2024-01-22 Abstract In recent years, the rapid advancements in big data, machine learning, and artificial intelligence have led to a corresponding rise in privacy concerns. One of the solutions to address these concerns is federated learning. In this thesis, we will look at the setting of vertical federated learning based on tree models. We have built a system that can do both entity resolution through private set intersection (PSI) and vertical federated learning (VFL). In this system, we have implemented an optimisation to pre-sort the data per feature before the start of VFL. We have also created a privacy framework, where we define four levels of privacy. This optimisation did not affect the privacy level of the system. In our results, we have seen that pre-sorting the data lowers the overall training time. How much depends on the number of entities and features of the passive party. We observe from our results that we estimate the speed-up to be 0.3654 seconds per feature and 0.2093 seconds per 1000 entities. Subject Vertical Federated LearningPrivate Set IntersectionPrivacy To reference this document use: http://resolver.tudelft.nl/uuid:0304a61b-14df-44a5-8a72-84b5ea5d1eb6 Part of collection Student theses Document type master thesis Rights © 2024 Martin Li Files PDF MSc_CS_Thesis_Martin_Li.pdf 1.46 MB Close viewer /islandora/object/uuid:0304a61b-14df-44a5-8a72-84b5ea5d1eb6/datastream/OBJ/view