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.
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References
Cox, I., Miller, M., Bloom, J., Fridrich, J., Kalker, T.: Digital Watermarking and Steganography, 2nd edn. Morgan Kaufmann Publishers Inc., San Francisco (2007)
Battelino, T., et al.: Clinical targets for continuous glucose monitoring data interpretation: recommendations from the international consensus on time in range. Diab. Care 42(8), 1593–1603 (2019)
Gringinger, E., Schuetz, C., Neumayr, B., Schrefl, M., Wilson, S.: Towards a value-added information layer for SWIM: the semantic container approach. In: 2018 Integrated Communications, Navigation, Surveillance Conference (ICNS), Herndon, VA, pp. 1–20 (2018)
Panah, A.S., Van Schyndel, R., Sellis, T., Bertino, E.: On the properties of non-media digital watermarking: a review of state of the art techniques. IEEE Access 4, 2670–2704 (2016)
Papadimitriou, P., Garcia-Molina, H.: Data leakage detection. IEEE Trans. Knowl. Data Eng. 23(1), 51–63 (2011)
Duy, T.P., Tran, D., Ma, W.: An intelligent learning-based watermarking scheme for outsourced biomedical time series data. In: 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, pp. 4408–4415 (2017)
Pham, T.D., Tran, D., Ma, W.: Ownership protection of outsourced biomedical time series data based on optimal watermarking scheme in data mining. Australas. J. Inf. Syst. 21 (2017). https://doi.org/10.3127/ajis.v21i0.1541
Ayday, E., Yilmaz, E., Yilmaz, A.: Robust optimization-based watermarking scheme for sequential data. In: 22nd International Symposium on Research in Attacks, Intrusions and Defenses, pp. 323–336 (2019)
Acknowledgments
Part of this work was conducted as part of the MyPCH project. This project received funding from the EU’s Horizon 2020 program for research and innovation, NGI_Trust funds via the Grant Agreement Number 825618.
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Gruber, S., Neumayr, B., Fabianek, C., Gringinger, E., Schuetz, C.G., Schrefl, M. (2021). Towards Informed Watermarking of Personal Health Sensor Data for Data Leakage Detection. In: Zhao, X., Shi, YQ., Piva, A., Kim, H.J. (eds) Digital Forensics and Watermarking. IWDW 2020. Lecture Notes in Computer Science(), vol 12617. Springer, Cham. https://doi.org/10.1007/978-3-030-69449-4_9
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DOI: https://doi.org/10.1007/978-3-030-69449-4_9
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