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Filtering by: Committee Member Cotilla-Sanchez, J. Eduardo Creator Alsulaimawi, Zahir Ahmed Hussein

A Non-negative Matrix Factorization Framework for Privacy-preserving and Federated Learning

Creator:

Alsulaimawi, Zahir Ahmed Hussein

Abstract:

The uncontrolled growth in domains such as surveillance systems, health care services, and finance produce a large amount of data and contain potentially sensitive data that can become public if they are not appropriately sanitized. Motivated by this issue, we introduce a privacy filter (PF), a novel non-negative matrix factorization...

Resource Type:

Masters Thesis
Information-theoretic Approach to Design and Evaluate Privacy-preserving and Fair Frameworks for Continuous High-dimensional Data

Creator:

Alsulaimawi, Zahir Ahmed Hussein

Abstract:

Deep learning is becoming the latest trend in sensitive applications, such as healthcare, criminal justice, and finance. As these new applications emerge, adversaries are circumventing them. Further, there have been concerns about the possibility of bias and discrimination in predictive applications. In order to address these issues, we propose an...

Resource Type:

Dissertation

Full Text:

perspectives. To my committee members, Dr. Ted Brekken and Dr. Eduardo Cotilla-Sanchez, I am profoundly