Private Mouse and Keyboard Behaviour Dataset
Description: Mouse and keyboard dataset can include sensitive personal data (i.e. login credentials, banking information, or text messages). Differential Privacy  allows data scientists to train behaviour models without collecting the raw inputs from users.
Goal: Allow data scientists to train models on mouse and keyboard data that they can’t see using differential privacy. Steps:
1- Deploy a domain node using HAGrid .
2- Deploy a network node that collects data from different domain nodes and handles the network requests using PySyft and PyGrid .
3- Data owners can upload datasets to domain nodes. Noise is added to data once uploaded via differential privacy.
4- Data scientists can log into the network, get a privacy budget and run machine learning models.
Supervisor: Mayar Elfares and Guanhua Zhang
Distribution: 20% Literature, 60% Implementation, 20% Analysis
Requirements: Good Python skills, good knowledge of operating systems and databases
 Dwork, Cynthia and Aaron Roth. 2014. The Algorithmic Foundations of Differential Privacy. Foundations and Trends in Theoretical Computer Science. 9(3-4), p.211-407.
 HAGRid: https://pypi.org/project/hagrid/
 PySyft: https://github.com/OpenMined/PySyft