Abstract
Deep learning has achieved remarkable success in the field of crowdsensing. The success of deep learning is inseparable from the amount of data. However, since the data uploaded by users involved in deep learning are usually correlated with individuals’ personal information, data owners may be reluctant to provide their data. Existing works on privacy-preserving deep learning primarily rely on fully homomorphic encryption primitives or oblivious transfer, which generates a lot of computation and communication costs to the participating entities. In this paper, we propose a non-interactive privacy-preserving deep learning scheme, named EPDL, to solve the above privacy and efficiency issues which means we can train the model more efficiently while protecting the privacy of image data. By employing a cloud platform and exploiting the homomorphic properties of an additively homomorphic cryptosystem, EPDL enables the deep learning models to be trained in an efficient and privacy-preserving manner without any data owner involved in the training process. Detailed security analysis demonstrates the privacy of data and models is safeguarded by EPDL. Extensive experiments based on real-world data sets show EPDL outperforms existing schemes whether in computation costs or communication overhead.
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Cheng N, Lyu F, Chen J, Xu W, Zhou H, Zhang S, Shen X (2018) Big data driven vehicular networks. IEEE Network 32(6):160–167
Cheng N, Lyu F, Quan W, Zhou C, He H, Shi W, Shen X (2019) Space/aerial-assisted computing offloading for iot applications: A learning-based approach. IEEE J Sel Areas Commun 37(5):1117–1129
Kaissis GA, Makowski MR, Rückert D, Braren RF (2020) Secure, privacy-preserving and federated machine learning in medical imaging. Nat Mach Intell 2(6):305–311
Laine S, Karras T, Aila T, Herva A, Saito S, Yu R, Li H, Lehtinen J (2017) Production-level facial performance capture using deep convolutional neural networks. In: Proceedings of the ACM SIGGRAPH/Eurographics symposium on computer animation, pp 1–10
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778
Shokri R, Shmatikov V (2015) Privacy-preserving deep learning. In: Proceedings of the 22nd ACM SIGSAC conference on computer and communications security, pp 1310–1321
Zhang C, Zhu L, Xu C, Liu X, Sharif K (2019) Reliable and privacy-preserving truth discovery for mobile crowdsensing systems. IEEE Trans Dependable Secure Comput. https://doi.org/10.1109/TDSC.2019.2919517
Shen D, Wu G, Suk HI (2017) Deep learning in medical image analysis. Annu Rev Biomed Eng 19:221–248
Zhang C, Zhu L, Xu C, Lu R (2018a) Ppdp: An efficient and privacy-preserving disease prediction scheme in cloud-based e-healthcare system. Future Generation Computer Systems 79:16–25
Newcomb A (2018) Facebook data harvesting scandal widens to 87 million people
Thompson SA, Warzel C (2019) The privacy project: Twelve million phones, one dataset, zero privacy. NY Times (Dec 19, 2019). https://nyti.ms/3geMYu4
Aono Y, Hayashi T, Wang L, Moriai S et al (2017) Privacy-preserving deep learning via additively homomorphic encryption. IEEE Trans Inf Forensics Secur 13(5):1333–1345
Gilad-Bachrach R, Dowlin N, Laine K, Lauter K, Naehrig M, Wernsing J (2016) Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy. In: International Conference on Machine Learning, pp 201–210
Zhang Q, Wang C, Wu H, Xin C, Phuong TV (2018b) Gelu-net: A globally encrypted, locally unencrypted deep neural network for privacy-preserved learning. In: IJCAI, pp 3933–3939
Abadi M, Chu A, Goodfellow I, McMahan HB, Mironov I, Talwar K, Zhang L (2016) Deep learning with differential privacy. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, pp 308–318
Phan N, Vu M, Liu Y, Jin R, Dou D, Wu X, Thai MT (2019) Heterogeneous gaussian mechanism: Preserving differential privacy in deep learning with provable robustness. arXiv preprint arXiv:190601444
Bonawitz K, Ivanov V, Kreuter B, Marcedone A, McMahan HB, Patel S, Ramage D, Segal A, Seth K (2017) Practical secure aggregation for privacy-preserving machine learning. In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, pp 1175–1191
Erlingsson Ú, Pihur V, Korolova A (2014) Rappor: Randomized aggregatable privacy-preserving ordinal response. In: Proceedings of the 2014 ACM SIGSAC conference on computer and communications security, pp 1054–1067
Huang C, Kairouz P, Chen X, Sankar L, Rajagopal R (2017) Context-aware generative adversarial privacy. Entropy 19(12):656
Aslett LJ, Esperança PM, Holmes CC (2015) Encrypted statistical machine learning: new privacy preserving methods. arXiv preprint arXiv:150806845
Hesamifard E, Takabi H, Ghasemi M (2017) Cryptodl: Deep neural networks over encrypted data. arXiv preprint arXiv:171105189
Li T, Li J, Chen X, Liu Z, Lou W, Hou T (2020) Npmml: A framework for non-interactive privacy-preserving multi-party machine learning. IEEE Transactions on Dependable and Secure Computing
Mohassel P, Zhang Y (2017) Secureml: A system for scalable privacy-preserving machine learning. In: 2017 IEEE Symposium on Security and Privacy (SP), IEEE, pp 19–38
Mohassel P, Rindal P (2018) Aby3: A mixed protocol framework for machine learning. In: Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, pp 35–52
Agrawal N, Shahin Shamsabadi A, Kusner MJ, Gascón A (2019) Quotient: two-party secure neural network training and prediction. In: Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, pp 1231–1247
Yuan J, Yu S (2013) Privacy preserving back-propagation neural network learning made practical with cloud computing. IEEE Trans Parallel Distrib Syst 25(1):212–221
Yonetani R, Naresh Boddeti V, Kitani KM, Sato Y (2017) Privacy-preserving visual learning using doubly permuted homomorphic encryption. In: Proceedings of the IEEE International Conference on Computer Vision, pp 2040–2050
Paillier P (1999) Public-key cryptosystems based on composite degree residuosity classes. In: International conference on the theory and applications of cryptographic techniques, Springer, pp 223–238
Wang C, Ren K, Wang J, Wang Q (2012) Harnessing the cloud for securely outsourcing large-scale systems of linear equations. IEEE Trans Parallel Distrib Syst 24(6):1172–1181
Bianchi T, Piva A, Barni M (2011) Analysis of the security of linear blinding techniques from an information theoretical point of view. 2011 IEEE International Conference on Acoustics. Speech and Signal Processing (ICASSP), IEEE, pp 5852–5855
Acknowledgements
This research is supported by the National Natural Science Foundation of China (Grant Nos. 61972037, U1804263, 61872041).
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Xu, C., Jin, G., Zhu, L. et al. EPDL: An efficient and privacy-preserving deep learning for crowdsensing. Peer-to-Peer Netw. Appl. 15, 2529–2541 (2022). https://doi.org/10.1007/s12083-022-01354-z
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DOI: https://doi.org/10.1007/s12083-022-01354-z