Skip to main content
Log in

Privacy-preserving image multi-classification deep learning model in robot system of industrial IoT

  • S.I. : Higher Level Artificial Neural Network Based Intelligent Systems
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Deep learning in robot systems is a popular application that can learn and train the results per requirements, but that collects sensitive information in the training process, easily causing leakage of users’ private information. To date, privacy-preserving deep learning models in robot systems have been sparsely researched. To solve the privacy leakage problem of deep learning in robot systems and fill the gap in robotics deep learning privacy research, in this paper a novel privacy-preserving image multi-classification deep-learning (PIDL) model in robot systems is presented. In PIDL, two schemes are proposed that adopt two groups of encrypted activation and cost functions—sigmoid plus cross-entropy function (PIDLSC) and softmax plus log-likelihood function (PIDLSL)—with secure calculation protocols, which are applied in a fog control center (FCC) with a non-colluding honest server by homomorphic encryption to improve the training efficiency, solve the encryption computation questions, and protect data and model privacy in robot systems. Security analysis and performance evaluation demonstrate that the proposed schemes realize security, correctness, and efficiency with low communication and computational costs.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Ping Y, Hao B, Li H, Lai Y et al (2019) Efficient training support vector clustering with appropriate boundary information. IEEE Access 7:146964–146978. https://doi.org/10.1109/ACCESS.2019.2945926

    Article  Google Scholar 

  2. Shen Z, Jiang H, Dong Q, Wang B (2020) Energy-efficient neighbor discovery for the Internet of Things. IEEE Internet Things J 7(1):684–698. https://doi.org/10.1109/JIOT.2019.2949922

    Article  Google Scholar 

  3. Luo X, Jiang C, Wang W, Xu Y, Wang J, Zhao W (2019) User behavior prediction in social networks using weighted extreme learning machine with distribution optimization. Future Gener Comput Syst 93:1023–1035. https://doi.org/10.1016/j.future.2018.04.085

    Article  Google Scholar 

  4. Chen M, Li Y, Luo X, Wang W, Wang L, Zhao W (2019) A novel human activity recognition scheme for smart health using multilayer extreme learning machine. IEEE Internet Things J 6(2):1410–1418. https://doi.org/10.1109/JIOT.2018.2856241

    Article  Google Scholar 

  5. Gorur K, Bozkurt MR, Bascil MS, Temurtas F (2019) GKP signal processing using deep CNN and SVM for tongue-machine interface. Traitement du Signal 36(4):319–329. https://doi.org/10.18280/ts.360404

    Article  Google Scholar 

  6. Meng WL, Mao CZ, Zhang J, Wen J, Wu DH (2019) A fast recognition algorithm of online social network images based on deep learning. Traitement du Signal 36(6):575–580. https://doi.org/10.18280/ts.360613

    Article  Google Scholar 

  7. Neelapu R, Devi GL, Rao KS (2018) Deep learning based conventional neural network architecture for medical image classification. Traitement du Signal 35(2):169–182. https://doi.org/10.3166/TS.35.169-182

    Article  Google Scholar 

  8. Zhang L, Ren J, Mu Y, Wang B (2020) Privacy-preserving multi-authority attribute-based data sharing framework for smart grid. IEEE Access 8:23294–23307. https://doi.org/10.1109/ACCESS.2020.2970272

    Article  Google Scholar 

  9. Qiu M, Dai H, Sangaiah AK, Liang K, Zheng X (2020) Guest editorial: special section on emerging privacy and security issues brought by artificial intelligence in industrial informatics. IEEE Trans Ind Inf 16(3):2029–2030. https://doi.org/10.1109/TII.2019.2953884

    Article  Google Scholar 

  10. Bai L, Du CL (2019) Design and simulation of a collision-free path planning algorithm for mobile robots based on improved ant colony optimization. Ing Syst d’ Inf 24(3):331–336. https://doi.org/10.18280/isi.240313

    Article  Google Scholar 

  11. Dieber B, Breiling B, Taurer S et al (2017) Security for the robot operating system. Robot Auton Syst 98:192–203. https://doi.org/10.1016/j.robot.2017.09.017

    Article  Google Scholar 

  12. Matellán V, Bonaci T, Sabaliauskaite G (2018) Cyber-security in robotics and autonomous systems. Robot Auton Syst 100:41–42

    Article  Google Scholar 

  13. Sabaliauskaite G, Ng GS, Ruths J (2016) Empirical assessment of methods to detect cyber attacks on a robot. In: 2016 IEEE 17th international symposium on high assurance systems engineering (HASE) pp 248–251. https://doi.org/10.1109/HASE.2016.19

  14. Dieber B, Kacianka S, Rass S et al (2016) Application-level Security for ROS-based Applications. In: IEEE/RSJ international conference on intelligent robots and systems (IROS), vol 10, pp 4477–4482. https://doi.org/10.1109/IROS.2016.7759659

  15. Breiling B, Dieber B, Schartner P (2017) Secure communication for the Robot Operating System. In: 2017 annual IEEE international systems conference (SysCon), pp 1–6. https://doi.org/10.1109/SYSCON.2017.7934755

  16. Martín F, Soriano E, Canas JM (2018) Quantitative analysis of security in distributed robotic frameworks. Robot Auton Syst 100:95–107

    Article  Google Scholar 

  17. Tonyali S, Munoz R, Akkaya K et al (2018) A realistic performance evaluation of privacy-preserving protocols for smart grid AMI networks. J Netw Comput Appl 119:24–41. https://doi.org/10.1016/j.jnca.2018.06.011

    Article  Google Scholar 

  18. Tonyali S et al (2018) Privacy-preserving protocols for secure and reliable data aggregation in IoT-enabled Smart Metering systems. Future Gener Comput Syst 78:547–557. https://doi.org/10.1016/j.future.2017.04.031

    Article  Google Scholar 

  19. Zhang T, Zhu Q (2017) Dynamic differential privacy for admm-based distributed classification learning. IEEE Trans Inf Forensics Secur 12(1):172–187. https://doi.org/10.1109/TIFS.2016.2607691

    Article  Google Scholar 

  20. Dwork C (2011) Differential privacy. Encyclopedia Crypto. Secur., pp 338–340

  21. Sweeney L (2002) K-anonymity: a model for protecting privacy. Int J Uncertain Fuzziness Knowl Based Syst 5(10):557–570. https://doi.org/10.1142/S0218488502001648

    Article  MathSciNet  MATH  Google Scholar 

  22. Phong LT, Aono Y, Hayashi T, Wang, and Moriai S, (2018) Privacy-preserving deep learning via additively homomorphic encryption. IEEE Trans Inf Forensics Secur 13(5):1333–1345. https://doi.org/10.1109/ARITH.2019.00047

    Article  Google Scholar 

  23. Phong LT, Phuong TT (2019) Privacy-preserving deep learning via weight transmission. IEEE Trans Inf Forensics Secur 14(11):3003–3015. https://doi.org/10.1109/TIFS.2019.2911169

    Article  Google Scholar 

  24. Zhang X, Chen X, Joseph KL, Xiang Y (2020) DeepPAR and DeepDPA: privacy-preserving and asynchronous deep learning for industrial IoT. IEEE Trans Ind Inf 16(3):2081–2090. https://doi.org/10.1109/TII.2019.2941244

    Article  Google Scholar 

  25. Ma X, Ma J, Li H, Jiang Q, Gao S (2018) PDLM: privacy-preserving deep learning model on cloud with multiple keys. IEEE Trans Serv Comput. https://doi.org/10.1109/TSC.2018.2868750

    Article  Google Scholar 

  26. Bost R, Popa RA, Tu S, Goldwasser, (2015) Machine learning classification over encrypted data. IACR Cryptology ePrint Archive, In: NDSS

  27. Baryalai M, Jang J, Jaccard, Liu D (2017) Towards privacy-preserving classification in neural networks. In: 2016 14th annual conference on privacy, security and trust (PST) vol 4. https://doi.org/10.1109/PST.2016.7906962

  28. Zhang Q, Yang LT, Chen Z (2016) Privacy preserving deep computation model on cloud for big data feature learning. IEEE Trans Comput 65(5):1351–1362. https://doi.org/10.1109/TC.2015.2470255

    Article  MathSciNet  MATH  Google Scholar 

  29. Chabanne H, Wargny DA, Milgram J, Morel etc C (2017) Privacy preserving classification on deep neural network. IACR Cryptology ePrint Archive 35

  30. Xie P, Bilenko M, Finley T, Gilad-Bachrach R, Lauter K, Naehrig M (2014) Crypto-nets: neural networks over encrypted data. arXiv preprint arXiv:1412.6181

  31. Wang B, Zhan Y, Zhang Z (2018) Cryptanalysis of a symmetric fully homomorphic encryption scheme. IEEE Trans Inf Forensics Secur 13(6):1460–1467. https://doi.org/10.1109/TIFS.2018.2790916

    Article  Google Scholar 

  32. Bourse F, Minelli M, Minihold M, Paillier P (2018) Fast homomorphic evaluation of deep discretized neural networks. CRYPTO 3:483–512. https://doi.org/10.1007/978-3-319-96878-0_17

    Article  MathSciNet  MATH  Google Scholar 

  33. Dowlin N, Gilad-Bachrach R, Laine K, Lauter K, Naehrig M, Wernsing J (2016) Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy. In: Proceedings of the International Conference on machine learning, pp 201–210

  34. Mohassel P, Zhang Y (2017) SecureML: a system for scalable privacy preserving machine learning. In: Proceedings of the 2017 IEEE symposium on security and privacy pp 19–38. https://doi.org/10.1109/SP.2017.12

  35. Liu J, Juuti M, Lu Y, Asokan N (2017) Oblivious neural network predictions via MiniONN transformations. In CCS, ACM

  36. Hesamifard E, Takabi H, Ghasemi M, Wright RN (2018) Privacy-preserving machine learning as a service. Proc Privacy Enhanc Technol 3:123–142. https://doi.org/10.1515/popets-2018-0024

    Article  Google Scholar 

  37. Bellafqira R, Coatrieux G, Genin E, Cozic M (2018) Secure multilayer perceptron based on homomorphic encryption. In: International workshop on digital watermarking (IWDW 2018) pp 322–336. https://doi.org/10.1007/978-3-030-11389-6_24

  38. Paillier P (1999) Public-key cryptosystems based on composite degree residuosity classes. EUROCRYRP’99 1592:223–238. https://doi.org/10.1007/3-540-48910-X_16

  39. Li P, Li J, Huang Z, Li T, Gaoa C, Yiu S, Chend K (2017) Multi-key privacy-preserving deep learning in cloud computing. Future Gener Comput Syst 74:76–85. https://doi.org/10.1016/j.future.2017.02.006

    Article  Google Scholar 

  40. Michael AN (2015) Neural networks and deep learning. Determination Press 2015

  41. Andreas B, Holger K (2015) An industrial application of behavior-oriented robotics in substation. In: Proceedings of IEEE international conference on robotics and automation, vol 1, pp. 749–754

  42. Qiu M, Kung SY, Gai K (2020) Intelligent security and optimization in Edge/Fog Computing. Future Gener Comput Syst 107:1140–1142. https://doi.org/10.1016/j.future.2019.06.002

    Article  Google Scholar 

  43. Bellafqira R, Coatrieux G, Genin E, Michel C (2018) Secure multilayer perceptron based on homomorphic encryption. In: Cryptography and security pp 322–336. https://doi.org/10.1007/978-3-030-11389-6_24

  44. Genocchi A (1884) Calcolo differenziale e principii di calcolo integrale. Bocca 1

Download references

Acknowledgements

This research was funded by the National Key R&D Program of China under Grant No. 2017YFB0802000, the National Natural Science Foundation of China under Grant Nos. U19B2021, U1736111, 61972457, the National Cryptography Development Fund under Grant No. MMJJ20180111, Key Technologies R&D Program of Henan Province under Grant No. 192102210295, Key Research and Development Program of Shaanxi under Grant No. 2020ZDLGY08-04, the Program for Science & Technology Innovation Talents in Universities of Henan Province under Grant No. 18HASTIT022.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zhili Zhang.

Ethics declarations

Conflict of interest

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, Y., Ping, Y., Zhang, Z. et al. Privacy-preserving image multi-classification deep learning model in robot system of industrial IoT. Neural Comput & Applic 33, 4677–4694 (2021). https://doi.org/10.1007/s00521-020-05426-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-020-05426-0

Keywords

Navigation