Abstract
With the increasing amount of data, data privacy has drawn great concern in machine learning among the public. Federated Learning, which is a new kind of distributed learning framework, enables data providers to train models locally to protect privacy. It solves the problem of privacy leakage of data by enabling multiple parties, each with their training dataset, to share the model instead of exchanging private data with the server side. However, there are still threats of data privacy leakage in federated learning. In this work, we are motivated to prevent GAN-based privacy inferring attacks in federated learning. For the GAN-based privacy inferring attacks, inspired by the idea of gradient compression, we propose a defense method called Federated Learning Parameter Compression (FLPC) which can reduce the sharing of information for privacy protection. It prevents attackers from recovering the privacy information of victims while maintaining the accuracy of the global model. Comprehensive experimental results demonstrated that our method is effective in the prevention of GAN-based privacy inferring attacks.
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References
Yan, K., Wang, X., Du, Y., Jin, N., Huang, H., Zhou, H.: Multi-step short-term power consumption forecasting with a hybrid deep learning strategy. Energies 11(11), 3089 (2018)
Wang, W., et al.: Hgate: Heterogeneous graph attention auto-encoders. IEEE Transactions on Knowledge and Data Engineering, pp. 1–1 (2021). https://doi.org/10.1109/TKDE.2021.3138788
Sharma, U., Maheshkar, S., Mishra, A.N., Kaushik, R.: Visual speech recognition using optical flow and hidden markov model. Wireless Pers. Commun. 106(4), 2129–2147 (2019)
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., Neubig, G.: Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. arXiv preprint arXiv:2107.13586 (2021)
Ranjan, R., Bansal, A., Zheng, J., Xu, H., Gleason, J., Lu, B., Nanduri, A., Chen, J.C., Castillo, C.D., Chellappa, R.: A fast and accurate system for face detection, identification, and verification. IEEE Trans. Biomet., Behav. Identity Sci. 1(2), 82–96 (2019)
Shokri, R., Shmatikov, V.: Privacy-preserving deep learning. In: Proceedings of the 22nd ACM SIGSAC conference on computer and communications security, pp. 1310–1321 (2015)
McMahan, B., Moore, E., Ramage, D., Hampson, S., y Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial intelligence and statistics, pp. 1273–1282. PMLR (2017)
Liu, M., Ho, S., Wang, M., Gao, L., Jin, Y., Zhang, H.: Federated learning meets natural language processing: A survey. arXiv preprint arXiv:2107.12603 (2021)
Hitaj, B., Ateniese, G., Perez-Cruz, F.: Deep models under the gan: information leakage from collaborative deep learning. In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, pp. 603–618 (2017)
Lyu, L., Yu, H., Ma, X., Sun, L., Zhao, J., Yang, Q., Yu, P.S.: Privacy and robustness in federated learning: Attacks and defenses. arXiv preprint arXiv:2012.06337 (2020)
Naseri, M., Hayes, J., De Cristofaro, E.: Toward robustness and privacy in federated learning: Experimenting with local and central differential privacy. arXiv e-prints pp. arXiv-2009 (2020)
Zhu, H., Xu, J., Liu, S., Jin, Y.: Federated learning on non-iid data: a survey. Neurocomputing 465, 371–390 (2021)
Saha, S., Ahmad, T.: Federated transfer learning: concept and applications. Intelligenza Artificiale 15(1), 35–44 (2021)
Maschler, B., Weyrich, M.: Deep transfer learning for industrial automation: a review and discussion of new techniques for data-driven machine learning. IEEE Ind. Electron. Mag. 15(2), 65–75 (2021)
Liu, P., Xu, X., Wang, W.: Threats, attacks and defenses to federated learning: issues, taxonomy and perspectives. Cybersecurity 5(1), 4 (2022)
Zhao, B., Mopuri, K.R., Bilen, H.: idlg: Improved deep leakage from gradients. arXiv preprint arXiv:2001.02610 (2020)
Sannai, A.: Reconstruction of training samples from loss functions. CoRR abs/1805.07337 (2018), http://arxiv.org/abs/1805.07337
Wang, Z., Song, M., Zhang, Z., Song, Y., Wang, Q., Qi, H.: Beyond inferring class representatives: User-level privacy leakage from federated learning. In: IEEE INFOCOM 2019-IEEE Conference on Computer Communications, pp. 2512–2520. IEEE (2019)
Fu, C., Zhang, X., Ji, S., Chen, J., Wu, J., Guo, S., Zhou, J., Liu, A.X., Wang, T.: Label inference attacks against vertical federated learning. In: 31st USENIX Security Symposium (USENIX Security 22), Boston, MA (2022)
Triastcyn, A., Faltings, B.: Federated learning with bayesian differential privacy. In: 2019 IEEE International Conference on Big Data (Big Data), pp. 2587–2596. IEEE (2019)
Shejwalkar, V., Houmansadr, A.: Manipulating the byzantine: Optimizing model poisoning attacks and defenses for federated learning. In: NDSS (2021)
Bagdasaryan, E., Veit, A., Hua, Y., Estrin, D., Shmatikov, V.: How to backdoor federated learning. In: International Conference on Artificial Intelligence and Statistics, pp. 2938–2948. PMLR (2020)
Blanchard, P., El Mhamdi, E.M., Guerraoui, R., Stainer, J.: Machine learning with adversaries: Byzantine tolerant gradient descent. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Guerraoui, R., Rouault, S., et al.: The hidden vulnerability of distributed learning in byzantium. In: International Conference on Machine Learning, pp. 3521–3530. PMLR (2018)
Yin, D., Chen, Y., Kannan, R., Bartlett, P.: Byzantine-robust distributed learning: Towards optimal statistical rates. In: International Conference on Machine Learning, pp. 5650–5659. PMLR (2018)
Fang, M., Cao, X., Jia, J., Gong, N.: Local model poisoning attacks to \(\{\)Byzantine-Robust\(\}\) federated learning. In: 29th USENIX Security Symposium (USENIX Security 20), pp. 1605–1622 (2020)
Wang, W., Wang, X., Feng, D., Liu, J., Han, Z., Zhang, X.: Exploring permission-induced risk in android applications for malicious application detection. IEEE Trans. Inf. Forensics Secur. 9(11), 1869–1882 (2014)
Wang, W., Zhao, M., Wang, J.: Effective android malware detection with a hybrid model based on deep autoencoder and convolutional neural network. J. Ambient Intell. Human. Comput. 10(8), 3035–3043 (2018)
Fan, M., Liu, J., Wang, W., Li, H., Tian, Z., Liu, T.: DAPASA: detecting android piggybacked apps through sensitive subgraph analysis. IEEE Trans. Inf. Forensics Secur. 12(8), 1772–1785 (2017)
Wang, W., Li, Y., Wang, X., Liu, J., Zhang, X.: Detecting android malicious apps and categorizing benign apps with ensemble of classifiers. Future Gener. Comput. Syst. 78, 987–994 (2018)
Su, D., Liu, J., Wang, W., Wang, X., Du, X., Guizani, M.: Discovering communities of malapps on android-based mobile cyber-physical systems. Ad Hoc Netw. 80, 104–115 (2018)
Wang, X., Wang, W., He, Y., Liu, J., Han, Z., Zhang, X.: Characterizing android apps’ behavior for effective detection of malapps at large scale. Future Gener. Comput. Syst. 75, 30–45 (2017)
Liu, X., Liu, J., Zhu, S., Wang, W., Zhang, X.: Privacy risk analysis and mitigation of analytics libraries in the android ecosystem. IEEE Trans. Mob. Comput. 19(5), 1184–1199 (2020)
Wang, W., Song, J., Xu, G., Li, Y., Wang, H., Su, C.: ContractWard: Automated vulnerability detection models for ethereum smart contracts. IEEE Trans. Netw. Sci. Eng. 8(2), 1133–1144 (2021)
Wang, W., Shang, Y., He, Y., Li, Y., Liu, J.: Botmark: automated botnet detection with hybrid analysis of flow-based and graph-based traffic behaviors. Inf. Sci. 511, 284–296 (2020)
Li, L., et al.: Creditcoin: a privacy-preserving blockchain-based incentive announcement network for communications of smart vehicles. IEEE Trans. Intell. Transp. Syst. 19(7), 2204–2220 (2018)
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, vol. 27 (2014)
Hinz, T., Fisher, M., Wang, O., Wermter, S.: Improved techniques for training single-image gans. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1300–1309 (2021)
Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., Aila, T.: Analyzing and improving the image quality of stylegan. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8110–8119 (2020)
Ding, M., et al.: Cogview: mastering text-to-image generation via transformers. Adv. Neural. Inf. Process. Syst. 34, 19822–19835 (2021)
Yan, X., Cui, B., Xu, Y., Shi, P., Wang, Z.: A method of information protection for collaborative deep learning under gan model attack. In: IEEE/ACM Transactions on Computational Biology and Bioinformatics (2019)
Luo, X., Zhu, X.: Exploiting defenses against gan-based feature inference attacks in federated learning. arXiv preprint arXiv:2004.12571 (2020)
Lin, Y., Han, S., Mao, H., Wang, Y., Dally, W.J.: Deep gradient compression: Reducing the communication bandwidth for distributed training. arXiv preprint arXiv:1712.01887 (2017)
Shi, S., Wang, Q., Chu, X., Li, B., Qin, Y., Liu, R., Zhao, X.: Communication-efficient distributed deep learning with merged gradient sparsification on gpus. In: IEEE INFOCOM 2020-IEEE Conference on Computer Communications, pp. 406–415. IEEE (2020)
Chen, C.Y., Choi, J., Brand, D., Agrawal, A., Zhang, W., Gopalakrishnan, K.: Adacomp: Adaptive residual gradient compression for data-parallel distributed training. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)
Deng, L.: The mnist database of handwritten digit images for machine learning research. IEEE Signal Process. Mag. 29(6), 141–142 (2012)
Acknowledgement
This work was supported in part by National Key R &D Program of China, under Grant 2020YFB2103802, in part by the National Natural Science Foundation of China, under grant U21A20463 and in part by the Fundamental Research Funds for the Central Universities of China under Grant KKJB320001536.
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Cao, H. et al. (2022). Prevention of GAN-Based Privacy Inferring Attacks Towards Federated Learning. In: Gao, H., Wang, X., Wei, W., Dagiuklas, T. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 461. Springer, Cham. https://doi.org/10.1007/978-3-031-24386-8_3
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DOI: https://doi.org/10.1007/978-3-031-24386-8_3
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