Skip to main content
SpringerLink
Log in

A Survey on Deep Learning Techniques for Privacy-Preserving

  • Conference paper
  • First Online:
Machine Learning for Cyber Security (ML4CS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 11806))

Included in the following conference series:

Abstract

There are challenges and issues when machine learning algorithm needs to access highly sensitive data for the training process. In order to address these issues, several privacy-preserving deep learning techniques, including Secure Multi-Party Computation and Homomorphic Encryption in Neural Network have been developed. There are also several methods to modify the Neural Network, so that it can be used in privacy-preserving environment. However, there is trade-off between privacy and performance among various techniques. In this paper, we discuss state-of-the-art of Privacy-Preserving Deep Learning, evaluate all methods, compare pros and cons of each approach, and address challenges and issues in the field of privacy-preserving by deep learning.

This work was partly supported by Indonesia Endowment Fund for Education (LPDP) and Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. 2017-0-00555, Towards Provable-secure Multi-party Authenticated Key Exchange Protocol based on Lattices in a Quantum World).

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

References

  1. Lazer, D., Pentland, A.S., Adamic, L., Aral, S., Barabasi, A.L.: Life in the network: the coming age of computational social science. Science 323, 721 (2009)

    Article  Google Scholar 

  2. Nasrabadi, N.M.: Pattern recognition and machine learning. J. Electron. Imaging 16, 049901 (2007)

    Article  Google Scholar 

  3. Chen, M., Hao, Y., Hwang, K., Wang, L.: Disease prediction by machine learning over big data from healthcare communities. IEEE Access 5, 8869–8879 (2017)

    Article  Google Scholar 

  4. Zhang, D., Chen, X., Wang, D., Shi, J.: A survey on collaborative deep learning and privacy-preserving. In: IEEE Third International Conference on Data Science in Cyberspace, pp. 652–658 (2018)

    Google Scholar 

  5. Meints, M., Moller, J.: Privacy-preserving data mining: a process centric view from a european perspective (2004)

    Google Scholar 

  6. Rivest, R.L., Adleman, L., Dertouzos, M.L.: On data banks and privacy homomorphisms. Found. Secure Comput. 4(11), 169–180 (1978)

    MathSciNet  Google Scholar 

  7. Paillier, P.: Public-key cryptosystems based on composite degree residuosity classes. In: Stern, J. (ed.) EUROCRYPT 1999. LNCS, vol. 1592, pp. 223–238. Springer, Heidelberg (1999). https://doi.org/10.1007/3-540-48910-X_16

    Chapter  Google Scholar 

  8. Gentry, C.: Fully homomorphic encryption using ideal lattices. In: Annual ACM on Symposium on Theory of Computing, pp. 169–178. ACM (2009)

    Google Scholar 

  9. Brakerski, Z., Vaikuntanathan, V.: Efficient fully homomorphic encryption from (Standard) LWE. SIAM J. Comput. 43(2), 831–871 (2014)

    Article  MathSciNet  Google Scholar 

  10. Brakerski, Z., Vaikuntanathan, V.: Fully homomorphic encryption from ring-LWE and security for key dependent messages. In: Rogaway, P. (ed.) CRYPTO 2011. LNCS, vol. 6841, pp. 505–524. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-22792-9_29

    Chapter  Google Scholar 

  11. Gentry, C., Sahai, A., Waters, B.: Homomorphic encryption from learning with errors: conceptually-simpler, asymptotically-faster, attribute-based. In: Canetti, R., Garay, J.A. (eds.) CRYPTO 2013. LNCS, vol. 8042, pp. 75–92. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40041-4_5

    Chapter  Google Scholar 

  12. Brakerski, Z., Gentry, C., Vaikuntanathan, V.: (Leveled) Fully homomorphic encryption without bootstrapping. ACM Transact. Comput. Theory (TOCT) 6(3), 13 (2014)

    MathSciNet  MATH  Google Scholar 

  13. Clear, M., McGoldrick, C.: Multi-identity and multi-key leveled FHE from learning with errors. In: Gennaro, R., Robshaw, M. (eds.) CRYPTO 2015. LNCS, vol. 9216, pp. 630–656. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-48000-7_31

    Chapter  Google Scholar 

  14. van Dijk, M., Gentry, C., Halevi, S., Vaikuntanathan, V.: Fully homomorphic encryption over the integers. In: Gilbert, H. (ed.) EUROCRYPT 2010. LNCS, vol. 6110, pp. 24–43. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13190-5_2

    Chapter  Google Scholar 

  15. Cheon, J.H., et al.: Batch fully homomorphic encryption over the integers. In: Johansson, T., Nguyen, P.Q. (eds.) EUROCRYPT 2013. LNCS, vol. 7881, pp. 315–335. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38348-9_20

    Chapter  Google Scholar 

  16. 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

    Chapter  MATH  Google Scholar 

  17. Ducas, L., Micciancio, D.: FHEW: bootstrapping homomorphic encryption in less than a second. In: Oswald, E., Fischlin, M. (eds.) EUROCRYPT 2015. LNCS, vol. 9056, pp. 617–640. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-46800-5_24

    Chapter  MATH  Google Scholar 

  18. 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

    Chapter  Google Scholar 

  19. Yao, A.C.-C.: How to generate and exchange secrets. In: Foundations of Computer Science 27th Annual Symposium, pp. 162–167. IEEE (1986)

    Google Scholar 

  20. Goldreich, O., Micali, S., Wigderson, A.: How to play any mental game. In: Proceedings of the Nineteenth Annual ACM Symposium on Theory of Computing, pp. 218–229. ACM (1987)

    Google Scholar 

  21. Dwork, C., McSherry, F., Nissim, K., Smith, A.: Calibrating noise to sensitivity in private data analysis. In: Halevi, S., Rabin, T. (eds.) TCC 2006. LNCS, vol. 3876, pp. 265–284. Springer, Heidelberg (2006). https://doi.org/10.1007/11681878_14

    Chapter  Google Scholar 

  22. Dwork, C.: Differential privacy. In: Bugliesi, M., Preneel, B., Sassone, V., Wegener, I. (eds.) ICALP 2006. LNCS, vol. 4052, pp. 1–12. Springer, Heidelberg (2006). https://doi.org/10.1007/11787006_1

    Chapter  Google Scholar 

  23. Chaudhuri, K., Monteleoni, C., Sarwate, A.D.: Differentially private empirical risk minimization. J. Mach. Learn. Res. 12, 1069–1109 (2011)

    MathSciNet  MATH  Google Scholar 

  24. Kifer, D., Smith, A., Thakurta, A.: Private convex empirical risk minimization and high-dimensional regression. In: Conference on Learning Theory, pp. 1–25 (2012)

    Google Scholar 

  25. Jagannathan, G., Pillaipakkamnatt, K., Wright, R.N.: A practical differentially private random decision tree classifier. In: IEEE International Conference on Data Mining Workshops 2009, ICDMW 2009, pp. 114–121. IEEE (2009)

    Google Scholar 

  26. LeCun, Y., Haffner, P., Bottou, L., Bengio, Y.: Object recognition with gradient-based learning. Shape, Contour and Grouping in Computer Vision. LNCS, vol. 1681, pp. 319–345. Springer, Heidelberg (1999). https://doi.org/10.1007/3-540-46805-6_19

    Chapter  Google Scholar 

  27. Goodfellow, I.: Generative adversarial nets. In: Advances in neural information processing systems, pp. 2672–2680 (2014)

    Google Scholar 

  28. Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv:1502.03167 (2015)

  29. Hesamifard, E., Takabi, H., Ghasemi, M.: CryptoDL: deep neural networks over encrypted data. arXiv:1711.05189 (2017)

  30. Liu, W., Pan, F., Wang, X.A., Cao, Y., Tang, D.: Privacy-preserving all convolutional net based on homomorphic encryption. In: International Conference on Network-Based Information Systems, pp. 752–762 (2018)

    Google Scholar 

  31. Graepel, T., Lauter, K., Naehrig, M.: ML confidential: machine learning on encrypted data. In: International Conference on Information Security and Cryptology, pp. 1–21 (2012)

    Google Scholar 

  32. Abadi, M., Erlingsson, U., Goodfellow, I.: On the protection of private information in machine learning systems: two recent approches. In: Computer Security Foundations Symposium, pp. 1–6 (2017)

    Google Scholar 

  33. Papernot, N., Abadi, M., Erlingsson, U.: Semi-supervised knowledge transfer for deep learning from private training data. arXiv:1610.05755 (2016)

  34. Gilad-Bachrach, R., Dowlin, N., Laine, K., Lauter, K., Naehrig, M., Wernsing, J.: Cryptonets: applying neural networks to encrypted data with high throughput and accuracy. In: International Conference on Machine Learning, pp. 201–210 (2016)

    Google Scholar 

  35. Chabanne, H., de Wargny, A., Milgram, J., Morel, C., Prouff, E.: Privacy-preserving classification on deep neural network. IACR Cryptology ePrint Archive (2017)

    Google Scholar 

  36. Mohassel, P., Zhang, Y.: SecureML: a system for scalable privacy-preserving machine learning, pp. 19–38 (2017)

    Google Scholar 

  37. Rabin, M.O.: How to exchange secrets with oblivious transfer. IACR Cryptology ePrint Archive, p. 187 (2005)

    Google Scholar 

  38. Shamir, A.: How to share a secret. Commun. ACM 22(11), 612–613 (1979)

    Article  MathSciNet  Google Scholar 

  39. Xue, H., et al.: Distributed large scale privacy-preserving deep mining. In: IEEE Third International Conference on Data Science in Cyberspace, pp. 418–422 (2018)

    Google Scholar 

  40. Rouhani, B., Riazi, M., Koushanfar, F.: DeepSecure: scalable provably-secure deep learning. In: 55th ACM/ESDA/IEEE Design Automation Conference, pp. 1–6 (2018)

    Google Scholar 

  41. Deng, L.: The MNIST database of handwritten digit images for machine learning research. IEEE Signal Process. Mag. 29, 141–142 (2012)

    Article  Google Scholar 

  42. Liu, J., Juuti, M., Lu, Y., Asokan, N.: Oblivious neural network predictions via MiniONN transformations. In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, pp. 619–631 (2017)

    Google Scholar 

  43. Juvekar, C., Vaikuntanathan, V., Chandrakasan, A.: GAZELLE: a low latency framework for secure neural network inference. In: 27th USENIX Security Symposium, pp. 1651–1669 (2018)

    Google Scholar 

  44. Sanyal, A., Kusner, M.J., Gascón, A., Kanade, V.: TAPAS: tricks to accelerate (Encrypted) prediction as a service. arXiv preprint, arXiv:1806.03461 (2018)

  45. Bourse, F., Minelli, M., Minihold, M., Paillier, P.: Fast homomorphic evaluation of deep discretized neural networks. In: Shacham, H., Boldyreva, A. (eds.) CRYPTO 2018. LNCS, vol. 10993, pp. 483–512. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-96878-0_17

    Chapter  Google Scholar 

  46. Mohassel, P., Rindal, P.: ABY 3: a mixed protocol framework for machine learning. In: Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, pp. 35–52. ACM (2018)

    Google Scholar 

  47. Jiang, X., Kim, M., Lauter, K., Song, Y.: Secure outsourced matrix computation and application to neural networks. In: Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, pp. 1209–1222. ACM (2018)

    Google Scholar 

  48. Phong, L.T., Aono, Y., Hayashi, T., Wang, L., Moriai, S.: Privacy-preserving deep learning via additively homomorphic encryption. In: IEEE Transactions on Information Forensics and Security, pp. 1333–1345. IEEE (2018)

    Google Scholar 

  49. Shokri, R., Shmatikov, V.: Privacy-preserving deep learning. In: Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, pp. 1310–1321. ACM (2015)

    Google Scholar 

  50. Zhang, Q., Yang, L.T., Castiglione, A., Chen, Z., Li, P.: Secure weighted possibilistic C-means algorithm on cloud for clustering big data. Inf. Sci. 479, 515–525 (2019)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computing, Korea Advanced Institute of Science and Technology (KAIST), 291 Gwahak-ro, Yuseong-gu, Daejeon, 34141, Korea

    Harry Chandra Tanuwidjaja, Rakyong Choi & Kwangjo Kim

Authors
  1. Harry Chandra Tanuwidjaja
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rakyong Choi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kwangjo Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kwangjo Kim .

Editor information

Editors and Affiliations

  1. Xidian University, Xi’an, China

    Xiaofeng Chen

  2. Fujian Normal University, Fuzhou, China

    Xinyi Huang

  3. Swinburne University of Technology, Hawthorn, VIC, Australia

    Jun Zhang

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

Tanuwidjaja, H.C., Choi, R., Kim, K. (2019). A Survey on Deep Learning Techniques for Privacy-Preserving. In: Chen, X., Huang, X., Zhang, J. (eds) Machine Learning for Cyber Security. ML4CS 2019. Lecture Notes in Computer Science(), vol 11806. Springer, Cham. https://doi.org/10.1007/978-3-030-30619-9_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-30619-9_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-30618-2

  • Online ISBN: 978-3-030-30619-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation