Type: dissertation
Abstract: Machine learning (ML) has emerged as a fundamental driver of technological progress across diverse domains, powering accurate predictions and data-driven decision-making.However, as ML models are increasingly learned on sensitive data such as medical records and private images, concerns over data privacy have grown significantly.A growing body of research indicates that these models may inadvertently reveal information about their training data through inference attacks.Within this context, we investigate how membership and property inference attacks exploit model behaviors to leak sensitive information at both the sample and distribution levels, and subsequently propose a risk measure to evaluate privacy risks across different models.First, we improve the efficiency of shadow model-based inference attacks in an attackagnostic manner.While shadow models are a common component in inference attacks, their reliance on training a large number of models incurs substantial computational overhead and limits their practicality.Such inefficiency mainly stems from the independent training and use of these shadow models.Regarding this problem, we present a novel shadow pool training framework, which constructs multiple shared models and trains them jointly within a single process.In particular, we leverage the Mixture-of-Experts mechanism as the shadow pool to interconnect individual models, enabling them to share sub-networks and improving efficiency.Secondly, we investigate a new attack surface in vertical federated learning (VFL), highlighting previously underexplored privacy leakage risks inherent to this setting.
Author: Bai, Li
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7217253247