An Innovative Algorithm for Privacy Protection in a Voice Disorder Detection System

  • Zulfiqar AliEmail author
  • Muhammad Imran
  • Wadood Abdul
  • Muhammad Shoaib
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 636)


Health information is critical for the patient and its unauthorized access may have server impact. With the advancement in the healthcare systems especially through the Internet of Things give rises to patient privacy. We developed a healthcare system that protects identity of patients using innovative zero-watermarking algorithm along with vocal fold disorders detection. To avoid audio signal distortion, proposed system embeds watermark in a secret key of identity by visual cryptography rather than audio signal. The secret shares generated through visual cryptography are inserted in the secret watermark key by computing the features of audio signals. The proposed technique is evaluated using audio samples taken from voice disorder database of the Massachusetts Eye and Ear Infirmary (MEEI). Experimental results prove that the proposed technique achieves imperceptibility with reliability to extract identity, unaffected disorder detection result with high robustness. The results are provided in form of Normalized Cross-Correlation (NCR), Bit Error Rate (BER), and Energy Ratio (ENR).


  1. 1.
    Gong, T., Huang, H., Li, P., Zhang, K., Jiang, H.: A medical healthcare system for privacy protection based on IoT. In: Paper Presented at the 2015 Seventh International Symposium on Parallel Architectures, Algorithms and Programming (2015)Google Scholar
  2. 2.
    Hsu, C.-L., Lee, M.-R., Su, C.-H.: The role of privacy protection in healthcare information systems adoption. J. Med. Syst. 37(5), 9966 (2013)CrossRefGoogle Scholar
  3. 3.
    Chatlani, N., Soraghan, J.J.: Local binary patterns for 1-D signal processing. In: Paper Presented at the 2010 18th European Signal Processing Conference (2010)Google Scholar
  4. 4.
    Houam, L., Hafiane, A., Boukrouche, A., Lespessailles, E., Jennane, R.: One dimensional local binary pattern for bone texture characterization. Pattern Anal. Appl. 17(1), 179–193 (2014)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Massachusette Eye & Ear Infirmry Voice & Speech LAB. Disordered Voice Database Model 4337 (Ver. 1.03) (1994)Google Scholar
  6. 6.
    Ali, Z., Elamvazuthi, I., Alsulaiman, M., Muhammad, G.: Detection of voice pathology using fractal dimension in a multiresolution analysis of normal and disordered speech signals. J. Med. Syst. 40(1), 20 (2015)CrossRefGoogle Scholar
  7. 7.
    Arias-LondoÃśo, J.D., Godino-Llorente, J.I., SÃąenz-LechÃşn, N., Osma-Ruiz, V., Castellanos-DomÃnguez, G.: An improved method for voice pathology detection by means of a HMM-based feature space transformation. Pattern Recogn. 43(9), 3100–3112 (2010)CrossRefGoogle Scholar
  8. 8.
    Godino-Llorente, J.I., Gomez-Vilda, P., Blanco-Velasco, M.: Dimensionality reduction of a pathological voice quality assessment system based on Gaussian mixture models and short-term cepstral parameters. IEEE Trans. Biomed. Eng. 53(10), 1943–1953 (2006)CrossRefGoogle Scholar
  9. 9.
    Markaki, M., Stylianou, Y.: Voice pathology detection and discrimination based on modulation spectral features. IEEE Trans. Audio Speech Lang. Process. 19(7), 1938–1948 (2011)CrossRefGoogle Scholar
  10. 10.
    Muhammad, G., Melhem, M.: Pathological voice detection and binary classification using MPEG-7 audio features. Biomed. Signal Process. Control 11, 1–9 (2014)CrossRefGoogle Scholar
  11. 11.
    Villa-Canas, T., Belalcazar-Bolamos, E., Bedoya-Jaramillo, S., Garces, J.F., Orozco-Arroyave, J.R., Arias-Londono, J.D., Vargas-Bonilla, J.F.: Automatic detection of laryngeal pathologies using cepstral analysis in Mel and Bark scales. In: Paper Presented at the XVII Symposium of Image, Signal Processing, and Artificial Vision (STSIVA), 12–14 September 2012Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Zulfiqar Ali
    • 1
    Email author
  • Muhammad Imran
    • 2
  • Wadood Abdul
    • 3
  • Muhammad Shoaib
    • 2
  1. 1.Digital Speech Processing Group, Department of Computer Engineering, College of Computer and Information ScienceKing Saud UniversityRiyadhSaudi Arabia
  2. 2.College of Computer and Information ScienceKing Saud UniversityRiyadhSaudi Arabia
  3. 3.Department of Computer Engineering, College of Computer and Information ScienceKing Saud UniversityRiyadhSaudi Arabia

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