POSTER: Data Leakage Detection for Health Information System based on Memory Introspection

Sanoop Mallissery; Min-Chieh Wu; Chun-An Bau; Guan-Zhang Huang; Chen-Yu Yang; Wei-Chun Lin; Yu-Sung Wu

doi:10.1145/3320269.3405437

POSTER: Data Leakage Detection for Health Information System based on Memory Introspection

Sanoop Mallissery
, Min-Chieh Wu
, Chun-An Bau
, Guan-Zhang Huang
, Chen-Yu Yang
, Wei-Chun Lin
, Yu-Sung Wu

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research

5 Citations (Scopus)

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.

Original language	English
Title of host publication	Proceedings of the 15th ACM Asia Conference on Computer and Communications Security
Editors	Guofei Gu, Guiseppe Ateniese
Place of Publication	New York NY USA
Publisher	Association for Computing Machinery (ACM)
Pages	898-900
Number of pages	3
ISBN (Electronic)	9781450367509
DOIs	https://doi.org/10.1145/3320269.3405437
Publication status	Published - 2020
Externally published	Yes
Event	ACM Asia Conference on Computer and Communications Security 2020 - Taipei, Taiwan Duration: 5 Oct 2020 → 9 Oct 2020 Conference number: 15th https://dl.acm.org/doi/proceedings/10.1145/3320269 (Proceedings) https://asiaccs2020.cs.nthu.edu.tw (Website)

Conference

Conference	ACM Asia Conference on Computer and Communications Security 2020
Abbreviated title	AsiaCCS 2020
Country/Territory	Taiwan
City	Taipei
Period	5/10/20 → 9/10/20
Internet address	https://dl.acm.org/doi/proceedings/10.1145/3320269 (Proceedings) https://asiaccs2020.cs.nthu.edu.tw (Website)

Keywords

convolutional neural networks
data privacy
electronic health record
health information system
memory inspection
virtualization

Access to Document

10.1145/3320269.3405437

Cite this

Mallissery, S., Wu, M.-C., Bau, C.-A., Huang, G.-Z., Yang, C.-Y., Lin, W.-C., & Wu, Y.-S. (2020). POSTER: Data Leakage Detection for Health Information System based on Memory Introspection. In G. Gu, & G. Ateniese (Eds.), Proceedings of the 15th ACM Asia Conference on Computer and Communications Security (pp. 898-900). Association for Computing Machinery (ACM). https://doi.org/10.1145/3320269.3405437

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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 Min-Chieh Wu and Chun-An Bau and Guan-Zhang Huang and Chen-Yu Yang and Wei-Chun Lin and Yu-Sung Wu",

note = "Publisher Copyright: {\textcopyright} 2020 Owner/Author.; ACM Asia Conference on Computer and Communications Security 2020, AsiaCCS 2020 ; Conference date: 05-10-2020 Through 09-10-2020",

year = "2020",

doi = "10.1145/3320269.3405437",

language = "English",

pages = "898--900",

editor = "Guofei Gu and Guiseppe Ateniese",

booktitle = "Proceedings of the 15th ACM Asia Conference on Computer and Communications Security",

publisher = "Association for Computing Machinery (ACM)",

address = "United States of America",

url = "https://dl.acm.org/doi/proceedings/10.1145/3320269, https://asiaccs2020.cs.nthu.edu.tw",

}

Mallissery, S, Wu, M-C, Bau, C-A, Huang, G-Z, Yang, C-Y, Lin, W-C & Wu, Y-S 2020, POSTER: Data Leakage Detection for Health Information System based on Memory Introspection. in G Gu & G Ateniese (eds), Proceedings of the 15th ACM Asia Conference on Computer and Communications Security. Association for Computing Machinery (ACM), New York NY USA, pp. 898-900, ACM Asia Conference on Computer and Communications Security 2020, Taipei, Taiwan, 5/10/20. https://doi.org/10.1145/3320269.3405437

POSTER: Data Leakage Detection for Health Information System based on Memory Introspection. / Mallissery, Sanoop; Wu, Min-Chieh; Bau, Chun-An et al.
Proceedings of the 15th ACM Asia Conference on Computer and Communications Security. ed. / Guofei Gu; Guiseppe Ateniese. New York NY USA: Association for Computing Machinery (ACM), 2020. p. 898-900.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research

TY - GEN

T1 - POSTER

T2 - ACM Asia Conference on Computer and Communications Security 2020

AU - Mallissery, Sanoop

AU - Wu, Min-Chieh

AU - Bau, Chun-An

AU - Huang, Guan-Zhang

AU - Yang, Chen-Yu

AU - Lin, Wei-Chun

AU - Wu, Yu-Sung

N1 - Conference code: 15th

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

KW - convolutional neural networks

KW - data privacy

KW - electronic health record

KW - health information system

KW - memory inspection

KW - virtualization

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DO - 10.1145/3320269.3405437

M3 - Conference Paper

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EP - 900

BT - Proceedings of the 15th ACM Asia Conference on Computer and Communications Security

A2 - Gu, Guofei

A2 - Ateniese, Guiseppe

PB - Association for Computing Machinery (ACM)

CY - New York NY USA

Y2 - 5 October 2020 through 9 October 2020

ER -

Mallissery S, Wu MC, Bau CA, Huang GZ, Yang CY, Lin WC et al. POSTER: Data Leakage Detection for Health Information System based on Memory Introspection. In Gu G, Ateniese G, editors, Proceedings of the 15th ACM Asia Conference on Computer and Communications Security. New York NY USA: Association for Computing Machinery (ACM). 2020. p. 898-900 doi: 10.1145/3320269.3405437

POSTER: Data Leakage Detection for Health Information System based on Memory Introspection

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Conference

Keywords

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