2023/06/05 by Trishita Tiwari, Tiwari, Trishita, Suchin Gururangan +17 · 4 citations
Computer Science · Medicine · Social Sciences · #Access Control and Trust #Artificial Intelligence in Healthcare and Education #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Privacy-Preserving Technologies in Data
paper · pdf · doi:10.48550/arxiv.2306.03235
openalex publication_date 2023/06/05 · openalex created_date 2023/06/09 · openalex updated_date 2026/07/28
In today's machine learning (ML) models, any part of the training data can affect the model output. This lack of control for information flow from training data to model output is a major obstacle in training models on sensitive data when access control only allows individual users to access a subset of data. To enable secure machine learning for access-controlled data, we propose the notion of information flow control for machine learning, and develop an extension to the Transformer language model architecture that strictly adheres to the IFC definition we propose. Our architecture controls information flow by limiting the influence of training data from each security domain to a single expert module, and only enables a subset of experts at inference time based on the access control policy.The evaluation using large text and code datasets show that our proposed parametric IFC architecture has minimal (1.9%) performance overhead and can significantly improve model accuracy (by 38% for the text dataset, and between 44%--62% for the code datasets) by enabling training on access-controlled data.