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theses

In the past two years, the pandemic has shown its severity and infectivity to the world. Although researchers have put a lot of effort into preventing and diminishing the spread of the pandemic, the current situation is gloomy. As a result, more and more companies and institutes offer remote working environments for their employees and students. However, remote working is only a temporary solution as it causes inefficiency and difficulties in coordinating workers, and everyone will go back to classrooms and offices in one day. Therefore, the key to returning to pre-pandemic life is that people need to control the risk of exposing themselves to viruses. In this thesis, we address practical challenges that block the way of implementing indoor risk assessment and access control in privacy-preserving manners. First, data regarding positive infections are sensitive, and they should remain private. Therefore, it is challenging to track users' activities while keeping them secrete for any third-party service provider. Second, since access control needs infection data and biometric data in some cases, the access control system should also be privacy-preserving. In other words, the main access control system should not know the current user's identity and the authentication result to prevent potential privacy leaks. In Chap2, we propose a novel framework for privacy-preserving access control framework and group facial authentication system. Furthermore, in Chap3, we propose a novel privacy-preserving risk assessment framework with Federated Learning (FL). Last but not least, in Chap4, we developed an Android application to show the practicality of the framework proposed in Chap3.

http://dissertations.umi.com/gsnb.rutgers:12000

M.S.

Includes bibliographical references

doi:10.7282/t3-06pz-4234

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