@inproceedings{46d990800d6f4940a6b0bfb675ff404e,
title = "POSTER: Data Leakage Detection for Health Information System based on Memory Introspection",
abstract = "The abundance of highly sensitive personal information in the Health Information System (HIS) has made it a prime target of data breach attacks. However, securing the system with existing Data Leakage Prevention (DLP) solutions is difficult due to a lack of security perimeter and diverse composition of software components. We propose the use of hypervisor-based memory introspection for implementing data leakage detection in such an environment. The approach looks for the presence of sensitive raw data in the memory of both the client machines and the server machines, transcending the dependence of pre-existing security perimeters. It is inherently compatible with different types of application software and robust against transport or at-rest data encryption. A prototype has been built on the Bareflank hypervisor and the OpenEMR platform. The evaluation results confirmed the effectiveness of the approach.",
keywords = "convolutional neural networks, data privacy, electronic health record, health information system, memory inspection, virtualization",
author = "Sanoop Mallissery and Wu, {Min Chieh} and Bau, {Chun An} and Huang, {Guan Zhang} and Yang, {Chen Yu} and Lin, {Wei Chun} and Wu, {Yu Sung}",
note = "Publisher Copyright: {\textcopyright} 2020 Owner/Author.; 15th ACM Asia Conference on Computer and Communications Security, ASIA CCS 2020 ; Conference date: 05-10-2020 Through 09-10-2020",
year = "2020",
month = oct,
day = "5",
doi = "10.1145/3320269.3405437",
language = "English",
series = "Proceedings of the 15th ACM Asia Conference on Computer and Communications Security, ASIA CCS 2020",
publisher = "Association for Computing Machinery, Inc",
pages = "898--900",
booktitle = "Proceedings of the 15th ACM Asia Conference on Computer and Communications Security, ASIA CCS 2020",
}