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 and 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	X. Zhao, Y.-Q. Shi, A. Piva, H. J. Kim
Publisher	Springer Verlag
Number of pages	15
ISBN (Print)	978-3-030-69449-4
DOIs	https://doi.org/10.1007/978-3-030-69449-4_9
Publication status	Published - Nov 2020

Publication series

Name	Lecture Notes in Computer Science (LNCS)

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. (2020). Towards Informed Watermarking of Personal Health Sensor Data for Data Leakage Detection. In X. Zhao, Y.-Q. Shi, A. Piva, H. J. Kim (Ed.), Proceedings of the 19th International Workshop on Digital-forensics and Watermarking (IWDW 2020), Nov 25-27, 2020, Melbourne, Australia (Lecture Notes in Computer Science (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 / X. Zhao, Y.-Q. Shi, A. Piva, H. J. Kim. Springer Verlag, 2020. (Lecture Notes in Computer Science (LNCS)).

@inproceedings{ae616fc9f50248968fd0dac1dc2aa489,

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 = "2020",

month = nov,

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

language = "English",

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

series = "Lecture Notes in Computer Science (LNCS)",

publisher = "Springer Verlag",

editor = "{X. Zhao, Y.-Q. Shi, A. Piva, H. J. Kim}",

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 2020, Towards Informed Watermarking of Personal Health Sensor Data for Data Leakage Detection. in X. Zhao, Y.-Q. Shi, A. Piva, H. J. Kim (ed.), Proceedings of the 19th International Workshop on Digital-forensics and Watermarking (IWDW 2020), Nov 25-27, 2020, Melbourne, Australia. Lecture Notes in Computer Science (LNCS), Springer Verlag. 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. / X. Zhao, Y.-Q. Shi, A. Piva, H. J. Kim. Springer Verlag, 2020. (Lecture Notes in Computer Science (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 - 2020/11

Y1 - 2020/11

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

UR - http://www.dke.jku.at/research/publications/index.xq

U2 - 10.1007/978-3-030-69449-4_9

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

M3 - Conference proceedings

SN - 978-3-030-69449-4

T3 - Lecture Notes in Computer Science (LNCS)

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

A2 - X. Zhao, Y.-Q. Shi, A. Piva, H. J. Kim, null

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 X. Zhao, Y.-Q. Shi, A. Piva, H. J. Kim, editor, Proceedings of the 19th International Workshop on Digital-forensics and Watermarking (IWDW 2020), Nov 25-27, 2020, Melbourne, Australia. Springer Verlag. 2020. (Lecture Notes in Computer Science (LNCS)). 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