Abstract
Applied deep learning technology in digital health care service is a potential way to tackle many issues that hospitals face, such as over health care requests, lack of doctors, and patient overload. But a conventional deep learning model needs to compute raw medical data for evaluating health information, which raises considerable concern about data privacy. This paper proposes an approach using homomorphic encryption to encrypt raw data to protect privacy while deep learning models can still perform computations over encrypted data. This approach can be applied to almost any digital health care service in which data providers want to ensure that no one can use their data without permission. We will focus on a particular use case (predict mental health based on phone usage routine) to represent the approach’s applicability. Our encryption model’s accuracy is similar to the non-encryption model’s (only 0.01% difference) and has practical performance.
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References
Chase, M., et al.: Security of homomorphic encryption, Technical report, HomomorphicEncryption.org, Redmond, WA, USA (2017)
Cheon, J.H., Kim, A., Kim, M., Song, Y.: Homomorphic encryption for arithmetic of approximate numbers. In: Takagi, T., Peyrin, T. (eds.) ASIACRYPT 2017. LNCS, vol. 10624, pp. 409–437. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70694-8_15
Chiu, C.-T., Chang, Y.-H., Chen, C.-C., Ko, M.-C., Li, C.-Y.: Mobile phone use and health symptoms in children. J. Formos. Med. Assoc. 114, 598–604 (2015)
Clarke, G., Harvey, A.G.: The complex role of sleep in adolescent depression. Child Adolesc. Psychiatr. Clin. 21, 385–400 (2012)
Do, Y.K., Shin, E., Bautista, M.A., Foo, K.: The associations between self-reported sleep duration and adolescent health outcomes: what is the role of time spent on internet use? Sleep Med. 14, 195–200 (2013)
Domoff, S.E., Borgen, A.L., Foley, R.P., Maffett, A.: Excessive use of mobile devices and children’s physical health. Hum. Behav. Emerg. Technol. 1, 169–175 (2019)
Ghosh, A.K., Badillo-Urquiola, K., Guha, S., LaViola Jr., J.J., Wisniewski, P.J.: Safety vs. surveillance: what children have to say about mobile apps for parental control. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2018)
Halevi, S., Shoup, V.: Algorithms in HElib. In: Garay, J.A., Gennaro, R. (eds.) CRYPTO 2014. LNCS, vol. 8616, pp. 554–571. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-44371-2_31
Heffer, T., Good, M., Daly, O., MacDonell, E., Willoughby, T.: The longitudinal association between social-media use and depressive symptoms among adolescents and young adults: An empirical reply to Twenge. Clin. Psychol. Sci. 7(2019), 462–470 (2018)
Livni, R., Shalev-Shwartz, S., Shamir, O.: On the computational efficiency of training neural networks. In: Advances in Neural Information Processing Systems, pp. 855–863 (2014)
Lyubashevsky, V., Peikert, C., Regev, O.: On ideal lattices and learning with errors over rings. In: Gilbert, H. (ed.) EUROCRYPT 2010. LNCS, vol. 6110, pp. 1–23. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13190-5_1
Rivest, R.L., Adleman, L., Dertouzos, M.L., et al.: On data banks and privacy homomorphisms. Found. Secure Comput. 4, 169–180 (1978)
Shochat, T., Cohen-Zion, M., Tzischinsky, O.: Functional consequences of inadequate sleep in adolescents: a systematic review. Sleep Med. Rev. 18, 75–87 (2014)
Tarokh, L., Saletin, J.M., Carskadon, M.A.: Sleep in adolescence: Physiology, cognition and mental health. Neurosci. Biobehav. Rev. 70, 182 (2016)
Twenge, J.M., Joiner, T.E., Rogers, M.L., Martin, G.N.: Increases in depressive symptoms, suicide-related outcomes, and suicide rates among us adolescents after 2010 and links to increased new media screen time. Clin. Psychol. Sci. 6, 3–17 (2018)
Xie, P., Bilenko, M., Finley, T., Gilad-Bachrach, R., Lauter, K., Naehrig, M.: Crypto-nets: neural networks over encrypted data. In: ICLR (2014)
Yu, D., Li, Y., Xu, F., Zhang, P., Kostakos, V.: Smartphone app usage prediction using points of interest. Proc. ACM Interact. Mob. Wearable Ubiquit. Technol. 1, 174 (2018)
Acknowledgement
This research is funded by Vietnam National University Ho Chi Minh City (VNU-HCM) under grant number NCM2021-20-02. We acknowledge Ho Chi Minh City University of Technology (HCMUT), VNU-HCM, for supporting this study. The authors would also like to thank Mr. Nguyen Ngoc Ky for his comments helping to improve the manuscript of this work significantly.
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Nguyen-Van, T. et al. (2022). A Homomorphic Encryption Approach for Privacy-Preserving Deep Learning in Digital Health Care Service. In: Nguyen, N.T., Tran, T.K., Tukayev, U., Hong, TP., Trawiński, B., Szczerbicki, E. (eds) Intelligent Information and Database Systems. ACIIDS 2022. Lecture Notes in Computer Science(), vol 13758. Springer, Cham. https://doi.org/10.1007/978-3-031-21967-2_42
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