Status: Assigned February 2021
Student: Jose Castro Elizondo
Human mobility can be modeled using smartphone data, however, such data represents sensitive information, and the collection of those threatens the privacy of the users involved. The recent introduction of federated learning, a privacy-preserving approach to build machine and deep learning models, represents a promising technique to solve the privacy issue.
In the context of this MSc thesis or UROP project, you will: (i) create an overview of privacy-aware human mobility modeling approaches; (ii) pre-preprocess/normalize datasets to a common format (e.g., ); ; (iii) create advanced privacy-aware models for human mobility modeling (e.g., Transformer networks  using federated learning ); (iv) compare the results to existing baselines (e.g., baselines based on RNNs ,); (v) summarize the results and propose future work.
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