1 citations · 2 across the 6 of their papers we have counts for
8 papers
Are LLMs Good Financial User Simulators? Multi-view Investor Logic Alignment (MILA)
Jiajie He, Jiangyuan Hong, Dongling Ni +2
Large language models (LLMs) are increasingly used as user simulators, yet it remains unclear whether their predictions faithfully reproduce the evolving decisions of individual us…
Secure-by-Disguise: A Systematic Evaluation of Image Disguising for Confidential Medical Image Modeling
Jason Rojas, Jiajie He, Yash Patel +3
Cloud-based deep learning enables large-scale medical image analysis but raises significant privacy concerns when sensitive patient images are outsourced for model development. Ima…
Membership Inference Attacks on Recommender System: A Survey
Jiajie He, Xintong Chen, Xinyang Fang +4
Recommender systems (RecSys) have been widely applied to various applications, including E-commerce, finance, healthcare, social media and have become increasingly influential in s…
Auditing Approximate Machine Unlearning for Differentially Private Models
Yuechun Gu, Jiajie He, Keke Chen
Approximate machine unlearning aims to remove the effect of specific data from trained models to ensure individuals' privacy. Existing methods focus on the removed records and assu…
Membership Inference Attacks on In-Context Examples in LLM-based Recommender Systems
Jiajie He, Min-Chun Chen, Xintong Chen +3
Large language models (LLMs) based recommender systems (RecSys) can adapt flexibly across different domains. It uses in-context learning (ICL), i.e., prompts, including sensitive h…
RecPS: Privacy Risk Scoring for Recommender Systems
Jiajie He, Yuechun Gu, Keke Chen
Recommender systems (RecSys) have become an essential component of many web applications. The core of the system is a recommendation model trained on highly sensitive user-item int…
FT-PrivacyScore: Personalized Privacy Scoring Service for Machine Learning Participation
Yuechun Gu, Jiajie He, Keke Chen
Training data privacy has been a top concern in AI modeling. While methods like differentiated private learning allow data contributors to quantify acceptable privacy loss, model u…
Adaptive Domain Inference Attack with Concept Hierarchy
Yuechun Gu, Jiajie He, Keke Chen
With increasingly deployed deep neural networks in sensitive application domains, such as healthcare and security, it's essential to understand what kind of sensitive information c…