Skip to main content
SpringerLink
Log in

Privacy-Preserving Collaborative Medical Time Series Analysis Based on Dynamic Time Warping

  • Conference paper
  • First Online:
Book cover Computer Security – ESORICS 2019 (ESORICS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 11736))

Included in the following conference series:

Abstract

Evaluating medical time series (e.g., physiological sequences) under dynamic time warping (DTW) derives insights assisting biomedical research and clinical decision making. Due to the natural distribution of medical data, a collaboration among multiple healthcare institutes is required to carry out a reliable and quality medical judgment. Yet sharing medical data cross the boundaries of multiple institutions faces widespread privacy threats, along with increasingly stringent laws and privacy regulations nowadays. Addressing such demands, we propose a privacy-preserving system tailored for the DTW-based analysis over the decentralized medical time series sequences. Our system constructs a secure and scalable architecture to deliver comprehensive results from a joint data analytic task with privacy preservation. To accelerate complicated DTW query processing, our system adapts the advancement in secure multi-party computation (MPC) framework to realize encrypted DTW computation, decomposing complicated and iterative operations into atomic functions under suitable MPC primitives and optimized for DTW. Moreover, our system introduces a secure hybrid pruning strategy that diminishes the volume of time series sequences that are submitted before and processed within the encrypted DTW query. We implement a prototype and evaluate its performance on Amazon Cloud. The empirical evaluation demonstrates the feasibility of our system in practice.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    [20] suggests that the OT-based Multiplication Triples generation is faster than the Homomorphic encryption-based protocol by up to three orders of magnitude.

  2. 2.

    The DTW query is the process to find the sequences similar to the query based on the DTW distance within a given threshold.

  3. 3.

    Another way is building a monolithic circuit to solve a decision tree. This is not under our consideration, since it leads higher latency.

References

  1. PhysioBank ATM. http://physionet.org/cgi-bin/atm/ATM

  2. UCR time series classification archive. https://www.cs.ucr.edu/~eamonn/time_series_data_2018/

  3. 104th United States Congress: Health Insurance Portability and Accountability Act of 1996 (HIPPA) (1996). https://www.hhs.gov/hipaa/index.html

  4. Aggarwal, G., et al.: Two can keep a secret: a distributed architecture for secure database services. In: Proceedings of CIDR (2005)

    Google Scholar 

  5. Asharov, G., Lindell, Y., Schneider, T., Zohner, M.: More efficient oblivious transfer and extensions for faster secure computation. In: Proceedings of ACM CCS (2013)

    Google Scholar 

  6. Atallah, M., Bykova, M., Li, J., Frikken, K., Topkara, M.: Private collaborative forecasting and benchmarking. In: Proceedings of WPES (2004)

    Google Scholar 

  7. Baldi, P., Baronio, R., De Cristofaro, E., Gasti, P., Tsudik, G.: Countering Gattaca: efficient and secure testing of fully-sequenced human genomes. In: Proceedings of ACM CCS (2011)

    Google Scholar 

  8. Barni, M., Failla, P., Lazzeretti, R., Sadeghi, A.R., Schneider, T.: Privacy-preserving ECG classification with branching programs and neural networks. IEEE TIFS 6, 452–468 (2011)

    Google Scholar 

  9. Beaver, D.: Efficient multiparty protocols using circuit randomization. In: Proceedings of Crypto (1991)

    Google Scholar 

  10. Begum, N., Ulanova, L., Wang, J., Keogh, E.: Accelerating dynamic time warping clustering with a novel admissible pruning strategy. In: Proceedings of ACM SIGKDD (2015)

    Google Scholar 

  11. Berndt, D.J., Clifford, J.: Using dynamic time warping to find patterns in time series. In: Proceedings of KDD Workshop (1994)

    Google Scholar 

  12. Blanton, M., Kang, A.R., Karan, S., Zola, J.: Privacy preserving analytics on distributed medical data. CoRR abs/1806.06477 (2018). http://arxiv.org/abs/1806.06477

  13. Bogdanov, D., Laud, P., Randmets, J.: Domain-polymorphic language for privacy-preserving applications. In: Proceedings of the ACM Workshop on Language Support for Privacy-Enhancing Technologies (2013)

    Google Scholar 

  14. Bogdanov, D., Laur, S., Willemson, J.: Sharemind: a framework for fast privacy-preserving computations. In: Proceedings of ESORICS (2008)

    Google Scholar 

  15. Brickell, J., Porter, D.E., Shmatikov, V., Witchel, E.: Privacy-preserving remote diagnostics. In: Proceedings of ACM CCS (2007)

    Google Scholar 

  16. Camara, C., Peris-Lopez, P., Tapiador, J.E.: Security and privacy issues in implantable medical devices: a comprehensive survey. J. Biomed. Inform. 55, 272–289 (2015)

    Article  Google Scholar 

  17. Canetti, R.: Universally composable security: a new paradigm for cryptographic protocols. Cryptology ePrint Archive, Report 2000/067 (2000)

    Google Scholar 

  18. Chen, Y., Hu, B., Keogh, E., Batista, G.E.: DTW-D: time series semi-supervised learning from a single example. In: Proceedings of ACM SIGKDD (2013)

    Google Scholar 

  19. Cho, H., Wu, D.J., Berger, B.: Secure genome-wide association analysis using multiparty computation. Nat. Biotechnol. 36(6), 547–551 (2018)

    Article  Google Scholar 

  20. Demmler, D., Schneider, T., Zohner, M.: ABY-a framework for efficient mixed-protocol secure two-party computation. In: Proceedings of NDSS (2015)

    Google Scholar 

  21. European Parliament and of the Council: The General Data Protection Regulation (GDPR) (2016). http://data.europa.eu/eli/reg/2016/679/2016-05-04

  22. Huang, Y., Malka, L., Evans, D., Katz, J.: Efficient privacy-preserving biometric identification. In: Proceedings of NDSS (2011)

    Google Scholar 

  23. Keogh, E.: Exact indexing of dynamic time warping. In: Proceedings of VLDB (2002)

    Chapter  Google Scholar 

  24. Kerschbaum, F., Schneider, T., Schröpfer, A.: Automatic protocol selection in secure two-party computations. In: Boureanu, I., Owesarski, P., Vaudenay, S. (eds.) ACNS 2014. LNCS, vol. 8479, pp. 566–584. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07536-5_33

    Chapter  Google Scholar 

  25. Lindell, Y., Pinkas, B.: Privacy preserving data mining. J. Cryptol. 15(3), 177–206 (2002)

    Article  MathSciNet  Google Scholar 

  26. Malkhi, D., Nisan, N., Pinkas, B., Sella, Y., et al.: Fairplay-secure two-party computation system. In: Proceedings of USENIX Security (2004)

    Google Scholar 

  27. Mohassel, P., Zhang, Y.: SecureML: a system for scalable privacy-preserving machine learning. In: Proceedings of IEEE S&P (2017)

    Google Scholar 

  28. Nikolaenko, V., Weinsberg, U., Ioannidis, S., Joye, M., Boneh, D., Taft, N.: Privacy-preserving ridge regression on hundreds of millions of records. In: Proceedings of IEEE S&P (2013)

    Google Scholar 

  29. Rakthanmanon, T., et al.: Searching and mining trillions of time series subsequences under dynamic time warping. In: Proceedings of ACM SIGKDD (2012)

    Google Scholar 

  30. Rodriguez, A., Laio, A.: Clustering by fast search and find of density peaks. Science 344(6191), 1492–1496 (2014)

    Article  Google Scholar 

  31. Salem, A., Berrang, P., Humbert, M., Backes, M.: Privacy-preserving similar patient queries for combined biomedical data. Proc. PETS 2019, 47–67 (2019)

    Google Scholar 

  32. Tkachenko, O., Weinert, C., Schneider, T., Hamacher, K.: Large-scale privacy-preserving statistical computations for distributed genome-wide association studies. In: Proceedings of ACM AsiaCCS (2018)

    Google Scholar 

  33. Wang, X.S., Huang, Y., Zhao, Y., Tang, H., Wang, X., Bu, D.: Efficient genome-wide, privacy-preserving similar patient query based on private edit distance. In: Proceedings of ACM CCS (2015)

    Google Scholar 

  34. Wang, X.: FlexSC (2018). https://github.com/wangxiao1254/FlexSC

  35. Yao, A.C.C.: How to generate and exchange secrets. In: Proceedings of IEEE FOCS (1986)

    Google Scholar 

  36. Yi, X., Bertino, E., Rao, F.Y., Bouguettaya, A.: Practical privacy-preserving user profile matching in social networks. In: Proceedings of IEEE ICDE (2016)

    Google Scholar 

  37. Zheng, W., Popa, R., Gonzalez, J.E., Stoica, I.: Helen: Maliciously secure coopetitive learning for linear models. In: Proceedings of IEEE S&P (2019)

    Google Scholar 

  38. Zheng, Y., Duan, H., Tang, X., Wang, C., Zhou, J.: Denoising in the dark: privacy-preserving deep neural network based image denoising. IEEE TDSC (2019)

    Google Scholar 

  39. Zheng, Y., Duan, H., Wang, C.: Learning the truth privately and confidently: encrypted confidence-aware truth discovery in mobile crowdsensing. IEEE TIFS 13(10), 2475–2489 (2018)

    Google Scholar 

  40. Zhu, H., Meng, X., Kollios, G.: Privacy preserving similarity evaluation of time series data. In: Proceedings of EDBT (2014)

    Google Scholar 

Download references

Acknowledgment

This work was supported by Australian Research Council Discovery and Linkage Projects (DP180103251 and LP160101766).

Author information

Authors and Affiliations

  1. RMIT University, Melbourne, Australia

    Xiaoning Liu & Xun Yi

Authors
  1. Xiaoning Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xun Yi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xun Yi .

Editor information

Editors and Affiliations

  1. NEC Corporation, Kawasaki, Japan

    Kazue Sako

  2. University of Surrey, Guildford, UK

    Steve Schneider

  3. University of Luxembourg, Esch-sur-Alzette, Luxembourg

    Peter Y. A. Ryan

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Liu, X., Yi, X. (2019). Privacy-Preserving Collaborative Medical Time Series Analysis Based on Dynamic Time Warping. In: Sako, K., Schneider, S., Ryan, P. (eds) Computer Security – ESORICS 2019. ESORICS 2019. Lecture Notes in Computer Science(), vol 11736. Springer, Cham. https://doi.org/10.1007/978-3-030-29962-0_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-29962-0_21

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-29961-3

  • Online ISBN: 978-3-030-29962-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation