8 papers
SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift
Jiaqi Zhu, Xincheng Chen, Yuncheng Wu +2
Generative models trained on a source domain often produce samples that are poorly aligned with shifted target domains, limiting their effectiveness for target-domain data augmenta…
Image Prompt Reconstruction Attacks on Distributed MLLM Inference Frameworks
Xinjian Luo, Hongyan Chang, Jianxin Wei +5
Distributed large language model (LLM) inference frameworks connect isolated consumer-grade devices for large-scale model inference, substantially reducing hardware constraints. Ho…
From Schema to Signal: Retrieval-Augmented Modeling for Relational Data Analytics
Lingze Zeng, Shaofeng Cai, Changshuo Liu +3
Relational data stored in RDBMS is foundational to many real-world applications across domains such as e-commerce, finance, and sociality. While deep neural networks (DNNs) have ac…
Modeling Concurrency Control as a Learnable Function
Hexiang Pan, Shaofeng Cai, Tien Tuan Anh Dinh +4
Concurrency control (CC) algorithms are important in modern transactional databases, as they enable high performance by executing transactions concurrently while ensuring correctne…
Towards Effective Orchestration of AI x DB Workloads
Naili Xing, Haotian Gao, Zhanhao Zhao +6
AI-driven analytics are increasingly crucial to data-centric decision-making. The practice of exporting data to machine learning runtimes incurs high overhead, limits robustness to…
Passive Inference Attacks on Split Learning via Adversarial Regularization
Xiaochen Zhu, Xinjian Luo, Yuncheng Wu +3
Split Learning (SL) has emerged as a practical and efficient alternative to traditional federated learning. While previous attempts to attack SL have often relied on overly strong…