Towards privacy-preserving forensic analysis for time-series medical data

Xiaoning Liu, Xingliang Yuan, Joseph Liu

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

Electronic medical record (EMR) forensics is at the forefront of both academia and industry, and has dominated increasingly important role in the fast revolutionized digital forensics area. Upon the severe financial loss and user privacy revealing caused by data breaches, protecting the forensic medical records only being mined by authorized investigators and data confidentiality is deemed essential. Standard encryption technique can ensure the end-to-end data security, yet restricting the functionality in forensic analyzing. How to proceed similarity match over forensic physiological data in a private manner is intrinsically challenging, because the natural properties of such medical data are high-dimensional and times series related. In this paper, we propose a secure framework to proceed similarity match over encrypted physiological time-series data. Our framework resorts to an advanced similarity search algorithm, aka stratified locality-sensitive hashing (SLSH) to assist an authorized forensic investigator to have in-depth understanding of physiological data with multiple perspectives. In addition, our framework adopts a scalable encrypted index construction which provides provable security guarantees. Finally, we give a discussion of our future work based on this framework. As a generic and scalable framework, our design can be easily extended to secure update and parallel processing.

Original languageEnglish
Title of host publicationProceedings - 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom 2018) - 12th IEEE International Conference on Big Data Science and Engineering (IEEE BigDataSE 2018)
Subtitle of host publication31 July–3 August 2018 New York, New York
EditorsKim-Kwang Raymond Choo, Yongxin Zhu, Zongming Fei, Bhavani Thuraisingham, Yang Xiang
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1664-1668
Number of pages5
ISBN (Electronic)9781538643884
ISBN (Print)9781538643877, 9781538643891
DOIs
Publication statusPublished - 2018
EventIEEE International Conference on Trust, Security and Privacy in Computing and Communications and IEEE International Conference on Big Data Science and Engineering (TrustCom/BigDataSE) 2018 - New York, United States of America
Duration: 31 Jul 20183 Aug 2018
Conference number: 17th
http://www.cloud-conf.net/trustcom18/

Conference

ConferenceIEEE International Conference on Trust, Security and Privacy in Computing and Communications and IEEE International Conference on Big Data Science and Engineering (TrustCom/BigDataSE) 2018
Abbreviated titleTrustCom 2018
CountryUnited States of America
CityNew York
Period31/07/183/08/18
Internet address

Keywords

  • Digital Forensics
  • Medical Data
  • Privacy Protection
  • Time-series Data

Cite this

Liu, X., Yuan, X., & Liu, J. (2018). Towards privacy-preserving forensic analysis for time-series medical data. In K-K. R. Choo, Y. Zhu, Z. Fei, B. Thuraisingham, & Y. Xiang (Eds.), Proceedings - 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom 2018) - 12th IEEE International Conference on Big Data Science and Engineering (IEEE BigDataSE 2018): 31 July–3 August 2018 New York, New York (pp. 1664-1668). [8456115] Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/TrustCom/BigDataSE.2018.00247
Liu, Xiaoning ; Yuan, Xingliang ; Liu, Joseph. / Towards privacy-preserving forensic analysis for time-series medical data. Proceedings - 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom 2018) - 12th IEEE International Conference on Big Data Science and Engineering (IEEE BigDataSE 2018): 31 July–3 August 2018 New York, New York. editor / Kim-Kwang Raymond Choo ; Yongxin Zhu ; Zongming Fei ; Bhavani Thuraisingham ; Yang Xiang. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2018. pp. 1664-1668
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title = "Towards privacy-preserving forensic analysis for time-series medical data",
abstract = "Electronic medical record (EMR) forensics is at the forefront of both academia and industry, and has dominated increasingly important role in the fast revolutionized digital forensics area. Upon the severe financial loss and user privacy revealing caused by data breaches, protecting the forensic medical records only being mined by authorized investigators and data confidentiality is deemed essential. Standard encryption technique can ensure the end-to-end data security, yet restricting the functionality in forensic analyzing. How to proceed similarity match over forensic physiological data in a private manner is intrinsically challenging, because the natural properties of such medical data are high-dimensional and times series related. In this paper, we propose a secure framework to proceed similarity match over encrypted physiological time-series data. Our framework resorts to an advanced similarity search algorithm, aka stratified locality-sensitive hashing (SLSH) to assist an authorized forensic investigator to have in-depth understanding of physiological data with multiple perspectives. In addition, our framework adopts a scalable encrypted index construction which provides provable security guarantees. Finally, we give a discussion of our future work based on this framework. As a generic and scalable framework, our design can be easily extended to secure update and parallel processing.",
keywords = "Digital Forensics, Medical Data, Privacy Protection, Time-series Data",
author = "Xiaoning Liu and Xingliang Yuan and Joseph Liu",
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Liu, X, Yuan, X & Liu, J 2018, Towards privacy-preserving forensic analysis for time-series medical data. in K-KR Choo, Y Zhu, Z Fei, B Thuraisingham & Y Xiang (eds), Proceedings - 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom 2018) - 12th IEEE International Conference on Big Data Science and Engineering (IEEE BigDataSE 2018): 31 July–3 August 2018 New York, New York., 8456115, IEEE, Institute of Electrical and Electronics Engineers, Piscataway NJ USA, pp. 1664-1668, IEEE International Conference on Trust, Security and Privacy in Computing and Communications and IEEE International Conference on Big Data Science and Engineering (TrustCom/BigDataSE) 2018, New York, United States of America, 31/07/18. https://doi.org/10.1109/TrustCom/BigDataSE.2018.00247

Towards privacy-preserving forensic analysis for time-series medical data. / Liu, Xiaoning; Yuan, Xingliang; Liu, Joseph.

Proceedings - 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom 2018) - 12th IEEE International Conference on Big Data Science and Engineering (IEEE BigDataSE 2018): 31 July–3 August 2018 New York, New York. ed. / Kim-Kwang Raymond Choo; Yongxin Zhu; Zongming Fei; Bhavani Thuraisingham; Yang Xiang. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2018. p. 1664-1668 8456115.

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

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T1 - Towards privacy-preserving forensic analysis for time-series medical data

AU - Liu, Xiaoning

AU - Yuan, Xingliang

AU - Liu, Joseph

PY - 2018

Y1 - 2018

N2 - Electronic medical record (EMR) forensics is at the forefront of both academia and industry, and has dominated increasingly important role in the fast revolutionized digital forensics area. Upon the severe financial loss and user privacy revealing caused by data breaches, protecting the forensic medical records only being mined by authorized investigators and data confidentiality is deemed essential. Standard encryption technique can ensure the end-to-end data security, yet restricting the functionality in forensic analyzing. How to proceed similarity match over forensic physiological data in a private manner is intrinsically challenging, because the natural properties of such medical data are high-dimensional and times series related. In this paper, we propose a secure framework to proceed similarity match over encrypted physiological time-series data. Our framework resorts to an advanced similarity search algorithm, aka stratified locality-sensitive hashing (SLSH) to assist an authorized forensic investigator to have in-depth understanding of physiological data with multiple perspectives. In addition, our framework adopts a scalable encrypted index construction which provides provable security guarantees. Finally, we give a discussion of our future work based on this framework. As a generic and scalable framework, our design can be easily extended to secure update and parallel processing.

AB - Electronic medical record (EMR) forensics is at the forefront of both academia and industry, and has dominated increasingly important role in the fast revolutionized digital forensics area. Upon the severe financial loss and user privacy revealing caused by data breaches, protecting the forensic medical records only being mined by authorized investigators and data confidentiality is deemed essential. Standard encryption technique can ensure the end-to-end data security, yet restricting the functionality in forensic analyzing. How to proceed similarity match over forensic physiological data in a private manner is intrinsically challenging, because the natural properties of such medical data are high-dimensional and times series related. In this paper, we propose a secure framework to proceed similarity match over encrypted physiological time-series data. Our framework resorts to an advanced similarity search algorithm, aka stratified locality-sensitive hashing (SLSH) to assist an authorized forensic investigator to have in-depth understanding of physiological data with multiple perspectives. In addition, our framework adopts a scalable encrypted index construction which provides provable security guarantees. Finally, we give a discussion of our future work based on this framework. As a generic and scalable framework, our design can be easily extended to secure update and parallel processing.

KW - Digital Forensics

KW - Medical Data

KW - Privacy Protection

KW - Time-series Data

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DO - 10.1109/TrustCom/BigDataSE.2018.00247

M3 - Conference Paper

SN - 9781538643877

SN - 9781538643891

SP - 1664

EP - 1668

BT - Proceedings - 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom 2018) - 12th IEEE International Conference on Big Data Science and Engineering (IEEE BigDataSE 2018)

A2 - Choo, Kim-Kwang Raymond

A2 - Zhu, Yongxin

A2 - Fei, Zongming

A2 - Thuraisingham, Bhavani

A2 - Xiang, Yang

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - Piscataway NJ USA

ER -

Liu X, Yuan X, Liu J. Towards privacy-preserving forensic analysis for time-series medical data. In Choo K-KR, Zhu Y, Fei Z, Thuraisingham B, Xiang Y, editors, Proceedings - 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom 2018) - 12th IEEE International Conference on Big Data Science and Engineering (IEEE BigDataSE 2018): 31 July–3 August 2018 New York, New York. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. 2018. p. 1664-1668. 8456115 https://doi.org/10.1109/TrustCom/BigDataSE.2018.00247