Skip to main content
Book cover

Protecting Privacy through Homomorphic Encryption pp 133–146Cite as

PRIORIS: Enabling Secure Detection of Suicidal Ideation from Speech Using Homomorphic Encryption

  • 705 Accesses

Abstract

Suicidal ideation is a major health concern in the United States, with many millions of people reporting experiencing serious suicidal thoughts each year. Early detection of suicidal thought is critical in preventing suicide attempts and treating affected individuals. Recent research has shown how machine learning can be used to detect suicidal ideation from phone speech data. However, given the very sensitive nature of the data involved in this process (i.e. phone conversations of at-risk persons and prediction results), it is difficult to imagine how such an application could be used in practice. To address this issue, we investigate a privacy-preserving variant of the ideation detection application flow involving homomorphic evaluation of neural networks. We describe multiple realistic use-cases to aid both affected individuals and clinical practitioners that would be enabled as a result of this secure infrastructure. We also give first order performance estimates for homomorphic evaluation of the networks proposed, and discuss various opportunities for further analysis.

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-77287-1_10
  • Chapter length: 14 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   59.99
Price excludes VAT (USA)
  • ISBN: 978-3-030-77287-1
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   79.99
Price excludes VAT (USA)
Hardcover Book
USD   109.99
Price excludes VAT (USA)
Learn about institutional subscriptions
Fig. 1
Fig. 2
Fig. 3

References

  1. Substance Abuse, Mental Health Services Administration, and Center for Behavioral Health Statistics. Suicidal thoughts and behavior among adults:results from the 2015 national survey on drug use and health.

    Google Scholar 

  2. Alon Brutzkus, Oren Elisha, and Ran Gilad-Bachrach. Low latency privacy preserving inference. In International Conference on Machine Learning, 2019.

    Google Scholar 

  3. Katie A Busch and Jan Fawcett. A fine-grained study of inpatients who commit suicide. Psychiatric Annals, 34(5):357–364, 2004.

    Google Scholar 

  4. Katie A Busch, Jan Fawcett, and Douglas G Jacobs. Clinical correlates of inpatient suicide. The Journal of clinical psychiatry, 2003.

    Google Scholar 

  5. Florian Bourse, Michele Minelli, Matthias Minihold, and Pascal Paillier. Fast homomorphic evaluation of deep discretized neural networks. In CRYPTO (3), volume 10993 of Lecture Notes in Computer Science, pages 483–512. Springer, 2018.

    Google Scholar 

  6. Louise Brådvik. Suicide risk and mental disorders, 2018.

    Google Scholar 

  7. Ewa K Czyz, Adam G Horwitz, Daniel Eisenberg, Anne Kramer, and Cheryl A King. Self-reported barriers to professional help seeking among college students at elevated risk for suicide, 2013.

    Google Scholar 

  8. Centers for Disease Control and Prevention. Webbased injury statistics query and reporting system (WISQARS). Online, accessed 2020-01-21. Available at URL: https://www.cdc.gov/injury/wisqars/index.html.

  9. Joseph C Franklin, Jessica D Ribeiro, Kathryn R Fox, Kate H Bentley, Evan M Kleiman, Xieyining Huang, Katherine M Musacchio, Adam C Jaroszewski, Bernard P Chang, and Matthew K Nock. Risk factors for suicidal thoughts and behaviors: a meta-analysis of 50 years of research. Psychological bulletin, 143(2):187, 2017.

    Google Scholar 

  10. Ran Gilad-Bachrach, Nathan Dowlin, Kim Laine, Kristin E. Lauter, Michael Naehrig, and John Wernsing. CryptoNets: Applying neural networks to encrypted data with high throughput and accuracy. In ICML, volume 48 of JMLR Workshop and Conference Proceedings, pages 201–210. JMLR.org, 2016.

    Google Scholar 

  11. John Gideon, Katie Matton, Steve Anderau, Melvin G McInnis, and Emily Mower Provost. When to intervene: Detecting abnormal mood using everyday smartphone conversations, 2019.

    Google Scholar 

  12. J. Gideon, M. McInnis, and E. Mower Provost. Improving cross-corpus speech emotion recognition with adversarial discriminative domain generalization (ADDoG). IEEE Transactions on Affective Computing, 2019.

    Google Scholar 

  13. John Gideon, Heather T Schatten, Melvin G McInnis, and Emily Mower Provost. Emotion recognition from natural phone conversations in individuals with and without recent suicidal ideation. In The 20th Annual Conference of the International Speech Communication Association INTERSPEECH 2019, 2019.

    Google Scholar 

  14. William Guy. ECDEU assessment manual for psychopharmacology. US Department of Health, Education, and Welfare, Public Health Service …, 1976.

    Google Scholar 

  15. Mehmet Sinan Inci, Berk Gulmezoglu, Gorka Irazoqui, Thomas Eisenbarth, and Berk Sunar. Cache attacks enable bulk key recovery on the cloud. In International Conference on Cryptographic Hardware and Embedded Systems, pages 368–388. Springer, 2016.

    Google Scholar 

  16. Erkki T Isometsä, Martti E Heikkinen, Mauri J Marttunen, Markus M Henriksson, Hillevi M Aro, and Jouko K Lönnqvist. The last appointment before suicide: is suicide intent communicated? The American journal of psychiatry, 1995.

    Google Scholar 

  17. Douglas G Jacobs, Ross J Baldessarini, Yeates Conwell, Jan A Fawcett, Leslie Horton, Herbert Meltzer, Cynthia R Pfeffer, and Robert I Simon. Assessment and treatment of patients with suicidal behaviors. 2010.

    Google Scholar 

  18. Z. N. Karam, E. M. Provost, S. Singh, J. Montgomery, C. Archer, G. Harrington, and M. G. Mcinnis. Ecologically valid long-term mood monitoring of individuals with bipolar disorder using speech. In 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 4858–4862, May 2014.

    Google Scholar 

  19. Zahi N Karam, Emily Mower Provost, Satinder Singh, Jennifer Montgomery, Christopher Archer, Gloria Harrington, and Melvin G Mcinnis. Ecologically valid long-term mood monitoring of individuals with bipolar disorder using speech. In 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 4858–4862. IEEE, 2014.

    Google Scholar 

  20. Charles R. Marmar, Adam D. Brown, Meng Qian, Eugene Laska, Carole Siegel, Meng Li, Duna Abu-Amara, Andreas Tsiartas, Colleen Richey, Jennifer Smith, Bruce Knoth, and Dimitra Vergyri. Speech-based markers for posttraumatic stress disorder in us veterans. Depression and Anxiety, 36(7):607–616, 2019.

    CrossRef  Google Scholar 

  21. Karthik Nandakumar, Nalini K. Ratha, Sharath Pankanti, and Shai Halevi. Towards deep neural network training on encrypted data. In CVPR Workshops, page 0. Computer Vision Foundation/IEEE, 2019.

    Google Scholar 

  22. Thomas Niederkrotenthaler, Daniel J Reidenberg, Benedikt Till, and Madelyn S Gould. Increasing help-seeking and referrals for individuals at risk for suicide by decreasing stigma: The role of mass media. American journal of preventive medicine, 47(3):S235–S243, 2014.

    Google Scholar 

  23. S. O. Sadjadi and J. H. L. Hansen. Unsupervised speech activity detection using voicing measures and perceptual spectral flux. IEEE Signal Processing Letters, 20(3):197–200, March 2013.

    CrossRef  Google Scholar 

  24. Aaron Segal, Antonio Marcedone, Benjamin Kreuter, Daniel Ramage, H. Brendan McMahan, Karn Seth, Keith Bonawitz, Sarvar Patel, and Vladimir Ivanov. Practical secure aggregation for privacy-preserving machine learning. In CCS, 2017.

    Google Scholar 

  25. Bing Xu, Naiyan Wang, Tianqi Chen, and Mu Li. Empirical evaluation of rectified activations in convolutional network. CoRR, abs/1505.00853, 2015.

    Google Scholar 

  26. Robert C Young, Jeffery T Biggs, Veronika E Ziegler, and Dolores A Meyer. A rating scale for mania: reliability, validity and sensitivity. The British journal of psychiatry, 133(5):429–435, 1978.

    Google Scholar 

  27. Yinqian Zhang, Ari Juels, Michael K Reiter, and Thomas Ristenpart. Cross-vm side channels and their use to extract private keys. In Proceedings of the 2012 ACM conference on Computer and communications security, pages 305–316, 2012.

    Google Scholar 

Download references

Acknowledgements

This work was created in collaboration with researchers at Microsoft. The authors would like to thank the organizers of Microsoft’s Private AI Bootcamp for their helpful feedback and reviews. The authors would also like to thank researchers in the Computational Human Artificial Intelligence (CHAI) Lab at the University of Michigan for their helpful discussions. (Note: Authors of this work are listed in contributing order.)

Author information

Authors and Affiliations

  1. University of Michigan, Ann Arbor, MI, USA

    Deepika Natarajan

  2. Aarhus University, Aarhus, Denmark

    Anders Dalskov

  3. Graz University of Technology, Graz, Austria

    Daniel Kales

  4. Centre for Secure Information Technologies (CSIT), Queen’s University Belfast, Belfast, UK

    Shabnam Khanna

Authors
  1. Deepika Natarajan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anders Dalskov
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Daniel Kales
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shabnam Khanna
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Deepika Natarajan .

Editor information

Editors and Affiliations

  1. West Coast Research Science, Facebook AI Research, Seattle, WA, USA

    Kristin Lauter

  2. Cryptography and Privacy Research Group, Microsoft Research, Redmond, WA, USA

    Wei Dai

  3. Cryptography and Privacy Research Group, Microsoft Research, Redmond, WA, USA

    Kim Laine

Rights and permissions

Reprints and Permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Verify currency and authenticity via CrossMark

Cite this chapter

Natarajan, D., Dalskov, A., Kales, D., Khanna, S. (2021). PRIORIS: Enabling Secure Detection of Suicidal Ideation from Speech Using Homomorphic Encryption. In: Lauter, K., Dai, W., Laine, K. (eds) Protecting Privacy through Homomorphic Encryption. Springer, Cham. https://doi.org/10.1007/978-3-030-77287-1_10

Download citation