Date of Award

2-12-2024

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Computer Science

First Advisor

John Talburt

Abstract

The recent years have witnessed a remarkable surge in the use of large-scale data analytics, significantly advancing research in data-driven fields [1, 2]. While these innovative technologies offer the promise of accelerated research, they concurrently introduce new security and privacy challenges, complicating collaborative efforts among researchers. These challenges are ubiquitous across various research domains, particularly in the realms of data collection, analysis, and collaboration [3, 4]. Predominantly, existing data collection and analytics platforms prioritize performance over security. This prioritization compels users to rely heavily on their collaborators for adhering to all relevant security and privacy protocols. Often, researchers find themselves in the position of having to develop additional infrastructure to ensure data protection and secure collaboration. Such requirements frequently lead to redundant work. Furthermore, despite these efforts, many of these systems still fall short in providing robust security and privacy assurances [5]. This deficiency not only erodes the confidence of participants but also complicates the process of gaining approval from institutional review boards. This scenario underscores the need for a more balanced approach in designing data analytics platforms, one that equally emphasizes security and efficiency.

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