자료유형  

 학위논문

Control Number  

0017161602

International Standard Book Number  

9798382652276

Dewey Decimal Classification Number  

004

Main Entry-Personal Name  

Feng, Tiantian.

Publication, Distribution, etc. (Imprint  

[S.l.] : University of Southern California., 2024

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2024

Physical Description  

105 p.

General Note  

Source: Dissertations Abstracts International, Volume: 85-11, Section: B.

General Note  

Advisor: Narayanan, Shrikanth.

Dissertation Note  

Thesis (Ph.D.)--University of Southern California, 2024.

Summary, Etc.  

요약Human-centered machine intelligence has revolutionized many leading domains, providing more intelligent services and applications in transportation, healthcare, and education. The advances in these fields profoundly change how people live, work, and interact with each other. These systems frequently utilize state-of-the-art machine learning (ML) algorithms to achieve a comprehensive understanding of human conditions, including how people perceive, feel, and interact with others, which provide possibilities to create technologies that increasingly augment human experiences. Despite promises human-centric ML systems deliver, they create critical risks in potentially leaking sensitive information that AI practitioners should consider protecting. The sensitive information can be demographics (e.g., age, gender), human states (e.g., health, emotions), or biometric fingerprints. In this thesis, I explore privacy-enhancing computation associated with human-centered ML. My thesis investigates established approaches to preserve privacy in diverse human-centered applications. However, we identify that these approaches are frequently ineffective when encountering limited data due to privacy restrictions in sensing, storing, and using such data. Concurrently, the generative foundation model is a rapidly evolving research field, leading to the success of modern generative AI capable of creating realistic and high-fidelity digital content. These advances in foundation models and generative AI also present opportunities for privacy-enhancing computing as high-quality generated content can serve as training data. This leads us to explore using the foundation model to generate training data to assist limited training encountered with sensitive data in human-centered applications. Our extensive experiments demonstrate the potential of the generative foundation model in assisting limited training caused by privacy constraints in obtaining human-centered signals. Moreover, we show that the generative foundation model can provide benefits to distributed learning algorithms, such as Federated Learning.

Subject Added Entry-Topical Term  

Computer science.

Subject Added Entry-Topical Term  

Computer engineering.

Index Term-Uncontrolled  

Foundation model

Index Term-Uncontrolled  

Generative models

Index Term-Uncontrolled  

Human-centered learning

Index Term-Uncontrolled  

Machine learning

Added Entry-Corporate Name  

University of Southern California Computer Science

Host Item Entry  

Dissertations Abstracts International. 85-11B.

Electronic Location and Access  

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Control Number  

joongbu:657302