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Representative Papers

Privacy in Personalized AI Is a System Property, Not Just a Model Property

Sep 29, 2026

This project addresses the limitation of single-model analyses in capturing system-level privacy risks within personalized AI by departing from traditional component-level paradigms to establish privacy as an emergent system property. Through systematic architectural analysis and privacy risk modeling, it identifies four distinct risk channels and constructs a multidimensional evaluation framework encompassing interaction trajectories, information flows, indirect leakage, and utility-privacy trade-offs. Ultimately, this work delivers actionable, systematized privacy auditing standards and an assessment methodology that effectively bridges critical gaps in existing audit approaches at the system level.

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To Share or Not to Share: Investigating Weight Sharing in Variational Graph Autoencoders

Feb 23, 2025

This work investigates the theoretical justification of weight sharing (WS) in Variational Graph Autoencoders (VGAEs). Through rigorous theoretical analysis and systematic experiments across multiple graph benchmarks—including Cora and Citeseer—we establish, for the first time, the universal benefits of WS in VGAEs: it substantially reduces model complexity, improves generalization, and preserves near-identical performance in link prediction and node classification. We reveal that WS serves a dual role—simplifying optimization and acting as an implicit regularizer—thereby enhancing embedding stability and robustness. Our findings demonstrate that WS is not merely an empirical heuristic but a principled design choice grounded in both theoretical analysis and empirical validation. This work establishes WS as a default architectural recommendation for VGAEs and related variational graph representation learning frameworks.

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Latest Papers

Privacy in Personalized AI Is a System Property, Not Just a Model Property

Sep 29, 2026

This project addresses the limitation of single-model analyses in capturing system-level privacy risks within personalized AI by departing from traditional component-level paradigms to establish privacy as an emergent system property. Through systematic architectural analysis and privacy risk modeling, it identifies four distinct risk channels and constructs a multidimensional evaluation framework encompassing interaction trajectories, information flows, indirect leakage, and utility-privacy trade-offs. Ultimately, this work delivers actionable, systematized privacy auditing standards and an assessment methodology that effectively bridges critical gaps in existing audit approaches at the system level.

0 citationsRead paper

To Share or Not to Share: Investigating Weight Sharing in Variational Graph Autoencoders

Feb 23, 2025

This work investigates the theoretical justification of weight sharing (WS) in Variational Graph Autoencoders (VGAEs). Through rigorous theoretical analysis and systematic experiments across multiple graph benchmarks—including Cora and Citeseer—we establish, for the first time, the universal benefits of WS in VGAEs: it substantially reduces model complexity, improves generalization, and preserves near-identical performance in link prediction and node classification. We reveal that WS serves a dual role—simplifying optimization and acting as an implicit regularizer—thereby enhancing embedding stability and robustness. Our findings demonstrate that WS is not merely an empirical heuristic but a principled design choice grounded in both theoretical analysis and empirical validation. This work establishes WS as a default architectural recommendation for VGAEs and related variational graph representation learning frameworks.

0 citationsRead paper