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Events for February
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PhD Thesis Proposal - Tingting Tang
Mon, Feb 03, 2025 @ 12:30 PM - 01:30 PM
Thomas Lord Department of Computer Science
University Calendar
Title: Optimizing Privacy-Preserving Machine Learning for Improved Privacy, Utility, and Efficiency Tradeoffs
Location: EEB 349
Date and Time: February 3, 2025, 12.30 PM-1.30 PM
Zoom Link: https://usc.zoom.us/j/7995244109?pwd=OUp6RWhUZlFGclgyN3hkREh0Z21ldz09
Committee: Murali Annavaram (Chair), Salman Avestimehr, Bhaskar Krishnamachari, Harsha Madhyastha, Sai Praneeth Karimireddy
Abstract: Privacy-preserving machine learning (PPML) is essential for protecting sensitive data in machine-learning applications, requiring a careful balance between privacy, utility, and efficiency. However, the trade-offs and interdependencies among these dimensions present significant design challenges. This thesis proposal explores and optimizes their interplay through low-rank decomposition, focusing on two key PPML technologies: Differential Privacy (DP) and Secure Multiparty Computation (MPC). In the context of DP-based graph neural networks (GNNs), I propose a novel training framework leveraging low-rank singular value perturbation to protect sensitive graph edges while preserving the primary graph structure. This approach achieves a significantly improved privacy-utility trade-off and demonstrates resilience to edge inference attacks. For MPC-based secure model inference, I propose leveraging low-rank decomposition for the linear layers of ML models, reducing the number of MPC multiplications required during offline and online phases. Techniques such as truncation skipping and linear layer concatenation further reduce computational and communication overheads, enhancing overall efficiency in MPC ML workflows without compromising the robust security guarantees provided by MPC. By addressing the interactions between privacy, utility, and efficiency, my proposal lays the foundation for more practical and effective deployment of privacy-preserving machine learning solutions in real-world applications.Location: Hughes Aircraft Electrical Engineering Center (EEB) - 349
Audiences: Everyone Is Invited
Contact: Tingting Tang
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PhD Dissertation Defense - Zihao He
Wed, Feb 05, 2025 @ 12:00 PM - 02:00 PM
Thomas Lord Department of Computer Science
University Calendar
Title: Aligning Large Language Models with Human Perspectives
Date & Time: Wednesday, February 5th - 12:00p - 2:00p
Location: RTH 306
Committee: Kristina Lerman (Chair, CS), Emilio Ferrara (CS), Marlon Twyman (Communication)
Abstract: Large Language Models (LLMs) are increasingly deployed in real-world applications. However, their ability to accurately represent diverse human perspectives remains a critical challenge. This thesis investigates LLM alignment, which refers to how closely these models reflect the ideologies, values, and communication styles of specific communities. First, I develop methods for aligning LLMs to online communities and introduce Community-Cross-Instruct, a framework that generates structured instruction-answer pairs to enhance fidelity and scalability. Second, I propose comprehensive evaluation frameworks to assess alignment beyond positional stances, including affective alignment (how well LLMs capture emotional and moral tones) and multidimensional evaluations across authenticity, toxicity, and harm. Finally, I explore ethical risks in alignment, demonstrating how minimal biased data during instruction tuning can shift an LLM’s behavior, raising concerns about ideological manipulation. These findings highlight the technical, evaluation, and ethical complexities of LLM alignment, providing a foundation for ensuring that LLMs reflect diverse human perspectives and stay robust to ideological manipulation.
Zoom Link: https://usc.zoom.us/j/97020518118?pwd=mZeDv2WhswDGTouNvvWFI9NFqhO5KR.1Location: Ronald Tutor Hall of Engineering (RTH) - 306
Audiences: Everyone Is Invited
Contact: Zihao He
