Towards Informed Watermarking of Personal Health Sensor Data for Data Leakage Detection

Sebastian Gruber; Bernd Neumayr; Christoph Fabianek; Eduard Gringinger; Christoph Georg Schütz; Michael Schrefl

doi:10.1007/978-3-030-69449-4_9

Towards Informed Watermarking of Personal Health Sensor Data for Data Leakage Detection

Sebastian Gruber
, Bernd Neumayr
, Christoph Fabianek
, Eduard Gringinger
, Christoph Georg Schütz
, Michael Schrefl

Institute of Business Informatics - Data & Knowledge Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

Abstract

Users of personal health devices want an easy way to permanently store their personal health sensor data and to share them with physicians and other authorized users, trusting that the data will not be disclosed to third parties. Digital watermarking for data leakage detection aims to prevent the unauthorized disclosure of data by imperceptibly marking the data for each authorized user, so that the authorized user can be identified as the data leaker and be held accountable. In this paper we present an approach for digital watermarking conceived as part of a personal health sensor data management platform. The approach comprises techniques for informed watermark embedding and non-blind watermark detection. Based on a proof-of-concept prototype, the approach is evaluated regarding configurability, robustness, and performance. Keywords: Medical Sensor Data, Digital Fingerprinting, Time Series Data

Original language	English
Title of host publication	Proceedings of the 19th International Workshop on Digital-forensics and Watermarking (IWDW 2020), Nov 25-27, 2020, Melbourne, Australia
Editors	Xianfeng Zhao, Yun-Qing Shi, Alessandro Piva, Hyoung Joong Kim
Publisher	Springer Verlag
Pages	109-124
Number of pages	16
ISBN (Print)	978-3-030-69449-4
DOIs	https://doi.org/10.1007/978-3-030-69449-4_9
Publication status	Published - 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12617 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Fields of science

102 Computer Sciences
102010 Database systems
102015 Information systems
102016 IT security
102025 Distributed systems
102027 Web engineering
102028 Knowledge engineering
102030 Semantic technologies
102033 Data mining
102035 Data science
502050 Business informatics
503008 E-learning

JKU Focus areas

Digital Transformation

Access to Document

10.1007/978-3-030-69449-4_9

Cite this

Gruber, S., Neumayr, B., Fabianek, C., Gringinger, E., Schütz, C. G., & Schrefl, M. (2021). Towards Informed Watermarking of Personal Health Sensor Data for Data Leakage Detection. In X. Zhao, Y.-Q. Shi, A. Piva, & H. J. Kim (Eds.), Proceedings of the 19th International Workshop on Digital-forensics and Watermarking (IWDW 2020), Nov 25-27, 2020, Melbourne, Australia (pp. 109-124). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12617 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-69449-4_9

Gruber, Sebastian ; Neumayr, Bernd ; Fabianek, Christoph et al. / Towards Informed Watermarking of Personal Health Sensor Data for Data Leakage Detection. Proceedings of the 19th International Workshop on Digital-forensics and Watermarking (IWDW 2020), Nov 25-27, 2020, Melbourne, Australia. editor / Xianfeng Zhao ; Yun-Qing Shi ; Alessandro Piva ; Hyoung Joong Kim. Springer Verlag, 2021. pp. 109-124 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Towards Informed Watermarking of Personal Health Sensor Data for Data Leakage Detection",

abstract = "Users of personal health devices want an easy way to permanently store their personal health sensor data and to share them with physicians and other authorized users, trusting that the data will not be disclosed to third parties. Digital watermarking for data leakage detection aims to prevent the unauthorized disclosure of data by imperceptibly marking the data for each authorized user, so that the authorized user can be identified as the data leaker and be held accountable. In this paper we present an approach for digital watermarking conceived as part of a personal health sensor data management platform. The approach comprises techniques for informed watermark embedding and non-blind watermark detection. Based on a proof-of-concept prototype, the approach is evaluated regarding configurability, robustness, and performance. Keywords: Medical Sensor Data, Digital Fingerprinting, Time Series Data",

author = "Sebastian Gruber and Bernd Neumayr and Christoph Fabianek and Eduard Gringinger and Sch{\"u}tz, \{Christoph Georg\} and Michael Schrefl",

year = "2021",

doi = "10.1007/978-3-030-69449-4\_9",

language = "English",

isbn = "978-3-030-69449-4",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "109--124",

editor = "Xianfeng Zhao and Yun-Qing Shi and Alessandro Piva and Kim, \{Hyoung Joong\}",

booktitle = "Proceedings of the 19th International Workshop on Digital-forensics and Watermarking (IWDW 2020), Nov 25-27, 2020, Melbourne, Australia",

}

Gruber, S, Neumayr, B, Fabianek, C, Gringinger, E, Schütz, CG & Schrefl, M 2021, Towards Informed Watermarking of Personal Health Sensor Data for Data Leakage Detection. in X Zhao, Y-Q Shi, A Piva & HJ Kim (eds), Proceedings of the 19th International Workshop on Digital-forensics and Watermarking (IWDW 2020), Nov 25-27, 2020, Melbourne, Australia. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12617 LNCS, Springer Verlag, pp. 109-124. https://doi.org/10.1007/978-3-030-69449-4_9

Towards Informed Watermarking of Personal Health Sensor Data for Data Leakage Detection. / Gruber, Sebastian; Neumayr, Bernd; Fabianek, Christoph et al.
Proceedings of the 19th International Workshop on Digital-forensics and Watermarking (IWDW 2020), Nov 25-27, 2020, Melbourne, Australia. ed. / Xianfeng Zhao; Yun-Qing Shi; Alessandro Piva; Hyoung Joong Kim. Springer Verlag, 2021. p. 109-124 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12617 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

TY - GEN

T1 - Towards Informed Watermarking of Personal Health Sensor Data for Data Leakage Detection

AU - Gruber, Sebastian

AU - Neumayr, Bernd

AU - Fabianek, Christoph

AU - Gringinger, Eduard

AU - Schütz, Christoph Georg

AU - Schrefl, Michael

PY - 2021

Y1 - 2021

N2 - Users of personal health devices want an easy way to permanently store their personal health sensor data and to share them with physicians and other authorized users, trusting that the data will not be disclosed to third parties. Digital watermarking for data leakage detection aims to prevent the unauthorized disclosure of data by imperceptibly marking the data for each authorized user, so that the authorized user can be identified as the data leaker and be held accountable. In this paper we present an approach for digital watermarking conceived as part of a personal health sensor data management platform. The approach comprises techniques for informed watermark embedding and non-blind watermark detection. Based on a proof-of-concept prototype, the approach is evaluated regarding configurability, robustness, and performance. Keywords: Medical Sensor Data, Digital Fingerprinting, Time Series Data

AB - Users of personal health devices want an easy way to permanently store their personal health sensor data and to share them with physicians and other authorized users, trusting that the data will not be disclosed to third parties. Digital watermarking for data leakage detection aims to prevent the unauthorized disclosure of data by imperceptibly marking the data for each authorized user, so that the authorized user can be identified as the data leaker and be held accountable. In this paper we present an approach for digital watermarking conceived as part of a personal health sensor data management platform. The approach comprises techniques for informed watermark embedding and non-blind watermark detection. Based on a proof-of-concept prototype, the approach is evaluated regarding configurability, robustness, and performance. Keywords: Medical Sensor Data, Digital Fingerprinting, Time Series Data

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DO - 10.1007/978-3-030-69449-4_9

M3 - Conference proceedings

SN - 978-3-030-69449-4

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 109

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BT - Proceedings of the 19th International Workshop on Digital-forensics and Watermarking (IWDW 2020), Nov 25-27, 2020, Melbourne, Australia

A2 - Zhao, Xianfeng

A2 - Shi, Yun-Qing

A2 - Piva, Alessandro

A2 - Kim, Hyoung Joong

PB - Springer Verlag

ER -

Gruber S, Neumayr B, Fabianek C, Gringinger E, Schütz CG , Schrefl M. Towards Informed Watermarking of Personal Health Sensor Data for Data Leakage Detection. In Zhao X, Shi YQ, Piva A, Kim HJ, editors, Proceedings of the 19th International Workshop on Digital-forensics and Watermarking (IWDW 2020), Nov 25-27, 2020, Melbourne, Australia. Springer Verlag. 2021. p. 109-124. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-69449-4_9

Towards Informed Watermarking of Personal Health Sensor Data for Data Leakage Detection

Abstract

Publication series

Fields of science

JKU Focus areas

Access to Document

Other files and links

MyPCH - My Personal Connected Health

Cite this

Towards Informed Watermarking of Personal Health Sensor Data for Data Leakage Detection

Abstract

Publication series

Fields of science

JKU Focus areas

Access to Document

Other files and links

Projects

MyPCH - My Personal Connected Health

Cite this