PriRepVGG: Privacy-Preserving 3-Party Inference Framework for Image-Based Defect Detection
Source abstract
Abstract
Image classification is widely used in industrial defect detection, medical diagnosis, social welfare, and other fields, in which privacy and security of models and data must be involved. For example, in diamond synthesis, the diamond substrate image annotation data and the defect detection model are of value for conservation. Based on ensuring inference efficiency and the security of these private data intellectual property, the 3-party secure inference based on secure multi-party computation (MPC) can be adopted. MPC allows parties to use neural networks while preserving their input privacy for collaborative computing, but it will lead to huge communication and memory consumption. This paper propose PriRepVGG, a lightweight privacy-preserving image-based defect detection framework for 3-party. In this work, firstly, This work optimized the division and added an AdaptiveAvgpool layer in MPC framework FALCON; then, This work ported the inference architecture of the RegVGG network into FALCON creatively. Our work applied PriRepVGG to the secure inference of the diamond substrates defect detection under the data server, model server, and compute server settings, which can be carried out in batches with a low misjudgment rate and verify the feasibility of image-based secure inference with a lightweight network in an industrial case under MPC.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Neural Network Applications · Brain Tumor Detection and Classification · Adversarial Robustness in Machine Learning
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Ningbo Artificial Intelligence Institute, Shanghai Jiao Tong University, Ningbo 315000, China
Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China
Zhengzhou Research Institute for Abrasives and Grinding, Zhengzhou 450001, China
Funding
National Major Science and Technology Projects of China
References (32)
Show source-provided references
1. Anomaly detection of defects on concrete structures with the convolutional autoencoder10.1016/j.aei.2020.101105 ↗
2. Kumar, N., Rathee, M., Chandran, N., Gupta, D., Rastogi, A., and Sharma, R. (2020, January 18–21). Cryptflow: Secure tensorflow inference. Proceedings of the 2020 IEEE Symposium on Security and Privacy (SP), San Francisco, CA, USA.10.1109/SP40000.2020.00092 ↗
3. Alvarez-Valle, J., Bhatu, P., Chandran, N., Gupta, D., Nori, A., Rastogi, A., Rathee, M., Sharma, R., and Ugare, S. (2020). Secure medical image analysis with cryptflow. arXiv.
4. SecureNN: 3-Party Secure Computation for Neural Network Training10.2478/popets-2019-0035 ↗
5. Mohassel, P., and Rindal, P. (2018, January 15–19). ABY3: A mixed protocol framework for machine learning. Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, Toronto, ON, Canada.
6. Liu, J., Juuti, M., Lu, Y., and Asokan, N. (November, January 30). Oblivious neural network predictions via minionn transformations. Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, Dallas, TX, USA.10.1145/3133956.3134056 ↗
7. Crypten: Secure multi-party computation meets machine learning
8. Makri, E., Rotaru, D., Smart, N.P., and Vercauteren, F. (2019). EPIC: Efficient private image classification (or: Learning from the masters). Cryptographers’ Track at the RSA Conference, Springer.10.1007/978-3-030-12612-4_24 ↗
9. Kodas, T.T., and Hampden-Smith, M.J. (2008). The Chemistry of Metal CVD, John Wiley & Sons.
10. Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012). 2012 AlexNet. Adv. Neural Inf. Process. Syst., 1–9.
11. Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv.
12. He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27–30). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.10.1109/CVPR.2016.90 ↗
13. Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv.
14. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018, January 18–23). Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00474 ↗
15. Zhang, X., Zhou, X., Lin, M., and Sun, J. (2018, January 18–23). Shufflenet: An extremely efficient convolutional neural network for mobile devices. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00716 ↗
16. Ding, X., Zhang, X., Ma, N., Han, J., Ding, G., and Sun, J. (2021, January 21–25). Repvgg: Making vgg-style convnets great again. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.10.1109/CVPR46437.2021.01352 ↗
17. Classification and recognition of encrypted EEG data based on neural network
18. Deep multi-task learning for malware image classification
19. DP-share: Privacy-preserving software defect prediction model sharing through differential privacy10.1007/s11390-019-1958-0 ↗
20. Wagh, S., Tople, S., Benhamouda, F., Kushilevitz, E., Mittal, P., and Rabin, T. (2020). Falcon: Honest-majority maliciously secure framework for private deep learning. arXiv.10.2478/popets-2021-0011 ↗
21. Riazi, M.S., Weinert, C., Tkachenko, O., Songhori, E.M., Schneider, T., and Koushanfar, F. (2018, January 4). Chameleon: A hybrid secure computation framework for machine learning applications. Proceedings of the 2018 on Asia Conference on Computer and Communications Security, Incheon, Korea.10.1145/3196494.3196522 ↗
22. Chandran, N., Gupta, D., Rastogi, A., Sharma, R., and Tripathi, S. (2019, January 17–19). EzPC: Programmable and efficient secure two-party computation for machine learning. Proceedings of the 2019 IEEE European Symposium on Security and Privacy (EuroS&P), Stockholm, Sweden.10.1109/EuroSP.2019.00043 ↗
23. Juvekar, C., Vaikuntanathan, V., and Chandrakasan, A. (2018, January 15–17). {GAZELLE}: A low latency framework for secure neural network inference. Proceedings of the 27th USENIX Security Symposium (USENIX Security 18), Baltimore, MD, USA.
24. Mohassel, P., and Zhang, Y. (2017, January 22–269). Secureml: A system for scalable privacy-preserving machine learning. Proceedings of the 2017 IEEE Symposium on Security and Privacy (SP), San Jose, CA, USA.10.1109/SP.2017.12 ↗
25. Wang, X., Malozemoff, A.J., and Katz, J. EMP-toolkit: Efficient MultiParty computation toolkit, 2016.
26. Patra, A., Schneider, T., Suresh, A., and Yalame, H. (2021, January 11–13). {ABY2. 0}: Improved {Mixed-Protocol} Secure {Two-Party} Computation. Proceedings of the 30th USENIX Security Symposium (USENIX Security 21), Online.
27. Rathee, D., Rathee, M., Goli, R.K.K., Gupta, D., Sharma, R., Chandran, N., and Rastogi, A. (2021, January 24–27). SiRnn: A math library for secure RNN inference. Proceedings of the 2021 IEEE Symposium on Security and Privacy (SP), Francisco, CA, USA.10.1109/SP40001.2021.00086 ↗
28. Catrina, O., and Saxena, A. (2010). Secure computation with fixed-point numbers. International Conference on Financial Cryptography and Data Security, Springer.10.1007/978-3-642-14577-3_6 ↗
29. Security and composition of multiparty cryptographic protocols10.1007/s001459910006 ↗
30. Canetti, R. (2001, January 8–11). Universally composable security: A new paradigm for cryptographic protocols. Proceedings of the Proceedings 42nd IEEE Symposium on Foundations of Computer Science, Newport Beach, CA, USA.10.1109/SFCS.2001.959888 ↗
31. Rosulek, M. The joy of cryptography. 2017.
32. Markstein, P. (2004, January 15–17). Software division and square root using Goldschmidt’s algorithms. Proceedings of the 6th Conference on Real Numbers and Computers (RNC’6), Wadern, Germany.