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Journal of Medical Systems

, Volume 33, Issue 4, pp 241–259 | Cite as

Embedding and Retrieving Private Metadata in Electrocardiograms

  • Suleyman S. Kozat
  • Michail Vlachos
  • Claudio Lucchese
  • Helga Van Herle
  • Philip S. Yu
Original Paper

Abstract

Due to the recent explosion of ‘identity theft’ cases, the safeguarding of private data has been the focus of many scientific efforts. Medical data contain a number of sensitive attributes, whose access the rightful owner would ideally like to disclose only to authorized personnel. One way of providing limited access to sensitive data is through means of encryption. In this work we follow a different path, by proposing the fusion of the sensitive metadata within the medical data. Our work is focused on medical time-series signals and in particular on Electrocardiograms (ECG). We present techniques that allow the embedding and retrieval of sensitive numerical data, such as the patient’s social security number or birth date, within the medical signal. The proposed technique not only allows the effective hiding of the sensitive metadata within the signal itself, but it additionally provides a way of authenticating the data ownership or providing assurances about the origin of the data. Our methodology builds upon watermarking notions, and presents the following desirable characteristics: (a) it does not distort important ECG characteristics, which are essential for proper medical diagnosis, (b) it allows not only the embedding but also the efficient retrieval of the embedded data, (c) it provides resilience and fault tolerance by employing multistage watermarks (both robust and fragile). Our experiments on real ECG data indicate the viability of the proposed scheme.

Keywords

ECG Fragile watermark Robust watermark 

References

  1. 1.
    da Silva, P. P., McGuinness, D. L., & McCool, R. (2003). Knowledge provenance infrastructure. Data Eng. Bull. 26(4), 26–32.Google Scholar
  2. 2.
    Cox, I. J., Kilian, J., Leighton, F. T., & Shamoon, T. (1997). Secure spread spectrum watermarking for multimedia. IEEE Trans. Image Process. 6(12), 1673–1687.CrossRefGoogle Scholar
  3. 3.
    Mihcak, M. K., Venkatesan, R., & Liu, T. (2005). Watermarking via optimization algorithms for quantizing randomized semi-global image statistics. ACM J. Multimedia Syst. 11(2), 185–200.CrossRefGoogle Scholar
  4. 4.
    Kong, X. (2001). Watermarking medical signals for telemedicine. IEEE Trans. Inf. Technol. Biomed. 5(3), 195–201.CrossRefGoogle Scholar
  5. 5.
    Engin, M., Cidam, O., & Engin, E. Z. (2005). Wavelet transformation based watermarking technique for human electrocardiogram. J. Med. Syst. 29(6), 589–594.CrossRefGoogle Scholar
  6. 6.
    Mihcak, M. K., Venkatesan, R., & Kesal, M. (2002). Cryptanalysis of discrete-sequence spread spectrum watermarks. In Proc. of the 5th Int. Workshop Inform. Hiding.Google Scholar
  7. 7.
    Sion, R., Atallah, M. J., & Prabhakar, S. (2006). Rights protection for discrete numeric streams. IEEE Trans. Knowl. Data Eng. 18(5), 699–714.CrossRefGoogle Scholar
  8. 8.
    Agrawal, R., & Kiernan, J. (2002). Watermarking relational databases. In Proc. of VLDB, (pp. 155–166).Google Scholar
  9. 9.
    Sion, R., Atallah, M., & Prabhakar, S. (2003). Rights protection for relational data. In Proc. of SIGMOD, (pp. 98–109).Google Scholar
  10. 10.
    Bertino, E., Ooi, B.-C., Yang, Y., & Deng, R. H. (2005). Privacy and ownership preserving of outsourced medical data. In Proc. of ICDE, (pp. 521–532).Google Scholar
  11. 11.
    Kargupta, H., Datta, S., Wang, Q., & Sivakumar, K. (2003) On the privacy preserving properties of random data perturbation techniques. In Proc. of ICDM, (pp. 99–106).Google Scholar
  12. 12.
    Liu, L., Kantarcioglu, M., & Thuraisingham, B. (2006). The applicability of the perturbation model-based privacy preserving data mining for real-world data. In Intl Workshop on Privacy Aspects of Data-Mining.Google Scholar
  13. 13.
    Oliveira, S., & Zaiane, O. (2003). Privacy preserving clustering by data transformation. In Proc. of SBBD, (pp. 304–318).Google Scholar
  14. 14.
    Vaidya, J., & Clifton, C. (2003). Privacy-preserving K-means clustering over vertically partitioned data. In Proc. of SIGKDD, (pp. 206–215).Google Scholar
  15. 15.
    Vaidya, J., & Clifton, C. (2004). Privacy preserving naive bayes classifier for vertically partitioned data. In Proc. of SDM.Google Scholar
  16. 16.
    Oppenheim, A. V., Willsky, A. S., & Nawab, S. H. (1997). Signals and systems, 2nd Edition. Englewood Cliffs: Prentice Hall.Google Scholar
  17. 17.
    Vincent Poor, H. (1994). An introduction to signal detection and estimation (2nd ed.). New York: Springer.zbMATHGoogle Scholar
  18. 18.
    Fridrich, J. (1998). Image watermarking for tamper detection. In Proc. ICIP, (pp. 404–408).Google Scholar
  19. 19.
    Wong, P. (1998). A public key watermark for image verification and authentication. In Proc. of Int. Conf. on Image Processing, (pp. 455–459).Google Scholar
  20. 20.
    Wolfgang, R. B., & Delp, E. J. (1999). Fragile watermarking using the VW2D watermark. In Proc. SPIE/IS T Inter. Conf. Security and Watermarking of Multimedia Contents, (pp. 204–213).Google Scholar
  21. 21.
    Moulin, P., Mihcak, M. E., & Lin, G.-I. (2000). An information-theoretic model for image watermarking and data hiding. In IEEE Int. Conf. on Image Processing.Google Scholar
  22. 22.
    Kozat, S. S., Venkatesan, R., & Mihcak, M. K. (2004). Robust hashing via matrix-invariances. In Proc. of IEEE Conf. on Image Processing.Google Scholar
  23. 23.
  24. 24.
    Petrucci, E., Balian, V., Filippini, G., & Mainardi, L. T. (2005). Atrial fibrillation detection algorithms for very long term ECG monitoring. In Computers in Cardiology.Google Scholar
  25. 25.
    Vlachos, M., Yu, P. S., & Castelli, V. (2005). On Periodicity detection and structural periodic Similarity. In Proc. of SDM.Google Scholar
  26. 26.
    Chiranjivi, G., Madasu, V. K., Hanmandlu, M., & Lovell, B. C. (2005). Arrhythmia detection in human electrocardiogram. In APRS Workshop on Digital Image Computing, (pp. 189–192).Google Scholar
  27. 27.
    Papoulis, A. (1977). Signal analysis. New York: McGraw-Hill.zbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Suleyman S. Kozat
    • 1
  • Michail Vlachos
    • 2
  • Claudio Lucchese
    • 3
  • Helga Van Herle
    • 4
  • Philip S. Yu
    • 5
  1. 1.Koç UniversityIstanbulTurkey
  2. 2.IBM ResearchZurichSwitzerland
  3. 3.I.S.T.I.-C.N.R.PisaItaly
  4. 4.David Geffen School of MedicineUCLALos AngelesUSA
  5. 5.University of ChicagoChicagoUSA

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