This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
With the bloom of machine learning, a massive amount of data has been used in the training process of machine learning. A tremendous amount of this data is user-generated data which allows the machine learning models to produce accurate results and personalized services. Nevertheless, I recognize the importance of preserving…
With the bloom of machine learning, a massive amount of data has been used in the training process of machine learning. A tremendous amount of this data is user-generated data which allows the machine learning models to produce accurate results and personalized services. Nevertheless, I recognize the importance of preserving the privacy of individuals by protecting their information in the training process. One privacy attack that affects individuals is the private attribute inference attack. The private attribute attack is the process of inferring individuals' information that they do not explicitly reveal, such as age, gender, location, and occupation. The impacts of this go beyond knowing the information as individuals face potential risks. Furthermore, some applications need sensitive data to train the models and predict helpful insights and figuring out how to build privacy-preserving machine learning models will increase the capabilities of these applications.However, improving privacy affects the data utility which leads to a dilemma between privacy and utility. The utility of the data is measured by the quality of the data for different tasks. This trade-off between privacy and utility needs to be maintained to satisfy the privacy requirement and the result quality. To achieve more scalable privacy-preserving machine learning models, I investigate the privacy risks that affect individuals' private information in distributed machine learning. Even though the distributed machine learning has been driven by privacy concerns, privacy issues have been
proposed in the literature which threaten individuals' privacy. In this dissertation, I investigate how to measure and protect individuals' privacy in centralized and distributed machine learning models. First, a privacy-preserving text representation learning is proposed to protect users' privacy that can be revealed from user generated data. Second, a novel privacy-preserving text classification for split learning is presented to improve users' privacy and retain high utility by defending against private attribute inference attacks.
