Han, QiweiMoretti, RodrigoBasto (Modatta), Eduardo PintoZe, Wu Zhen2022-06-172022-06-172022-01-212021-12-17http://hdl.handle.net/10362/140159This work project intends to propose a privacy-based system to Modatta, a start-up focused on monetising users' data, eliminating the concerns of data leakage. The system consists of the following techniques: Deep Interest Network(DIN)/ Hyperbolic Embedding(HE), Generative Adversarial Network(GAN) and Federated Learning(FL), providing a recommender system and protecting the users' privacy. Data protection has been a hotly debated topic in society for many years, especially the adverse social effects caused by the misuse of user privacy by technology giants. This report will show that GAN is one of the feasible solutions to tackle these concerns.engMachine learningDeep learningHyperbolic embeddingsData monetizationRecommender systemGenerative adversarial networkSynthetic dataBusiness analysismaster thesis202997286