Matthijs Marinus Dieperink
ABSTRACT
Data privacy and security are currently an important societal topic that rightfully garners much attention. In an effort to make people the owners of their personal data, the Bubl platform will provide its users with a secure personal data vault. This platform will have a focus on medical and healthcare data, because of its sensitive nature. Gathering important insights from users’ data could be useful, but due to the high privacy and security standards required by Bubl, it becomes impossible to deploy standard Machine Learning (ML) techniques. These methods require centralization of all training data, which is not allowed. This problem can be solved using techniques from the research field of Privacy Preserving Machine Learning (PPML). Therefore, a system to facilitate PPML within the Bubl platform is developed. More specifically, we employ a technique called Federated Learning (FL). In our FL implementation in the Bubl platform, we focus on minimizing Random-Access Memory (RAM) usage to adhere to the constraints posed by the small computational budgets of the data vaults. Challenges that arise are non-Independent and Identically Distributed (IID) data and the fact that patient vaults contain very few data samples. The latter is the main focus of this research as it is underdeveloped in the FL literature. Currently, we are still working on acquiring the results which are expected in the coming months. At this moment, only preliminary results are discussed that reflect on the effect of the number of clients and the distribution of non-IID data on the ML performance.
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Index Terms
- Federated Learning in the Bubl Platform to Enhance the Privacy of Personal Patient Data
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