research-article

Federated Dynamic Graph Neural Networks with Secure Aggregation for Video-based Distributed Surveillance

Authors:

Tong ZhaoAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 13, Issue 4

Article No.: 56, Pages 1 - 23

https://doi.org/10.1145/3501808

Published: 03 May 2022 Publication History

Abstract

Distributed surveillance systems have the ability to detect, track, and snapshot objects moving around in a certain space. The systems generate video data from multiple personal devices or street cameras. Intelligent video-analysis models are needed to learn dynamic representation of the objects for detection and tracking. Can we exploit the structural and dynamic information without storing the spatiotemporal video data at a central server that leads to a violation of user privacy? In this work, we introduce Federated Dynamic Graph Neural Network (Feddy), a distributed and secured framework to learn the object representations from graph sequences: (1) It aggregates structural information from nearby objects in the current graph as well as dynamic information from those in the previous graph. It uses a self-supervised loss of predicting the trajectories of objects. (2) It is trained in a federated learning manner. The centrally located server sends the model to user devices. Local models on the respective user devices learn and periodically send their learning to the central server without ever exposing the user’s data to server. (3) Studies showed that the aggregated parameters could be inspected though decrypted when broadcast to clients for model synchronizing, after the server performed a weighted average. We design an appropriate aggregation mechanism of secure aggregation primitives that can protect the security and privacy in federated learning with scalability. Experiments on four video camera datasets as well as simulation demonstrate that Feddy achieves great effectiveness and security.

References

[1]

Martin Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. 2016. Deep learning with differential privacy. In Proceedings of the ACM SIGSAC Conference on Computer and Communications Security. 308–318.

Digital Library

[2]

Prabhanjan Ananth and Abhishek Jain. 2015. Indistinguishability obfuscation from compact functional encryption. In Proceedings of the Annual Cryptology Conference. Springer, 308–326.

Digital Library

[3]

Elaine Barker and Allen Roginsky. 2018. Transitioning the Use of Cryptographic Algorithms and Key Lengths. Technical Report. National Institute of Standards and Technology.

[4]

Fabrice Benhamouda, Marc Joye, and Benoît Libert. 2016. A new framework for privacy-preserving aggregation of time-series data. ACM Trans. Inf. Syst. Secur. 18, 3 (2016), 1–21.

Digital Library

[5]

Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloe Kiddon, Jakub Konecny, Stefano Mazzocchi, H. Brendan McMahan, et al. 2019. Towards federated learning at scale: System design. arXiv preprint arXiv:1902.01046 (2019).

[6]

Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H. Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth. 2016. Practical secure aggregation for federated learning on user-held data. In Proceedings of the NIPS Workshop on Private Multi-party Machine Learning.

[7]

Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H. Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth. 2017. Practical secure aggregation for privacy-preserving machine learning. In Proceedings of the ACM SIGSAC Conference on Computer and Communications Security. 1175–1191.

Digital Library

[8]

Dan Boneh. 1998. The decision Diffie-Hellman problem. In Proceedings of the International Algorithmic Number Theory Symposium. Springer, 48–63.

[9]

Elette Boyle, Niv Gilboa, and Yuval Ishai. 2017. Group-based secure computation: Optimizing rounds, communication, and computation. In Proceedings of the Eurocrypt Conference. Springer, 163–193.

[10]

Michael Bramberger, Andreas Doblander, Arnold Maier, Bernhard Rinner, and Helmut Schwabach. 2006. Distributed embedded smart cameras for surveillance applications. Computer 39, 2 (2006), 68–75.

Digital Library

[11]

Hao Chen, Ilaria Chillotti, and Yongsoo Song. 2019. Multi-key homomorphic encryption from TFHE. In Proceedings of the International Conference on the Theory and Application of Cryptology and Information Security. Springer, 446–472.

Digital Library

[12]

Jianguo Chen, Kenli Li, Qingying Deng, Keqin Li, and S. Yu Philip. 2019. Distributed deep learning model for intelligent video surveillance systems with edge computing. IEEE Trans. Industr. Inform. (2019). DOI:

[13]

Jean-Sébastien Coron. 2000. On the exact security of full domain hash. In Proceedings of the Annual International Cryptology Conference. Springer, 229–235.

[14]

Ivan Damgård and Gert Læssøe Mikkelsen. 2010. Efficient, robust and constant-round distributed RSA key generation. In Proceedings of the Theory of Cryptography Conference. Springer, 183–200.

Digital Library

[15]

Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. In Proceedings of the Conference on Advances in Neural Information Processing Systems. 3844–3852.

[16]

Wenliang Du, Yunghsiang S. Han, and Shigang Chen. 2004. Privacy-preserving multivariate statistical analysis: Linear regression and classification. In Proceedings of the SIAM International Conference on Data Mining. SIAM, 222–233.

[17]

Matt Fredrikson, Somesh Jha, and Thomas Ristenpart. 2015. Model inversion attacks that exploit confidence information and basic countermeasures. In Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security. 1322–1333.

Digital Library

[18]

S. Dov Gordon, Feng-Hao Liu, and Elaine Shi. 2015. Constant-round MPC with fairness and guarantee of output delivery. In Proceedings of the CRYPTO Conference. Springer, 63–82.

Digital Library

[19]

Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Proceedings of the Conference on Advances in Neural Information Processing Systems. 1024–1034.

[20]

William L. Hamilton, Rex Ying, and Jure Leskovec. 2017. Representation learning on graphs: Methods and applications. arXiv preprint arXiv:1709.05584 (2017).

[21]

Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Richard Nock, Giorgio Patrini, Guillaume Smith, and Brian Thorne. 2017. Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption. arXiv preprint arXiv:1711.10677 (2017).

[22]

Chaoyang He, Keshav Balasubramanian, Emir Ceyani, Yu Rong, Peilin Zhao, Junzhou Huang, Murali Annavaram, and Salman Avestimehr. 2021. FedGraphNN: A federated learning system and benchmark for graph neural networks. arXiv preprint arXiv:2104.07145 (2021).

[23]

Mikael Henaff, Joan Bruna, and Yann LeCun. 2015. Deep convolutional networks on graph-structured data. arXiv preprint arXiv:1506.05163 (2015).

[24]

Rui Hu, Yuanxiong Guo, Hongning Li, Qingqi Pei, and Yanmin Gong. 2020. Personalized federated learning with differential privacy. IEEE Internet Things J. 7, 10 (2020), 9530–9539.

[25]

Di Jiang, Yuanfeng Song, Yongxin Tong, Xueyang Wu, Weiwei Zhao, Qian Xu, and Qiang Yang. 2019. Federated topic modeling. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 1071–1080.

Digital Library

[26]

Meng Jiang, Taeho Jung, Ryan Karl, and Tong Zhao. 2020. Federated dynamic GNN with secure aggregation. arXiv preprint arXiv:2009.07351 (2020).

[27]

Marc Joye and Benoît Libert. 2013. A scalable scheme for privacy-preserving aggregation of time-series data. In Proceedings of the International Conference on Financial Cryptography and Data Security. Springer, 111–125.

[28]

Taeho Jung, Junze Han, and Xiang-Yang Li. 2016. PDA: Semantically secure time-series data analytics with dynamic user groups. IEEE Trans. Depend. Sec. Comput. 15, 2 (2016), 260–274.

[29]

Taeho Jung, Xiang-Yang Li, and Meng Wan. 2014. Collusion-tolerable privacy-preserving sum and product calculation without secure channel. IEEE Trans. Depend. Sec. Comput. 12, 1 (2014), 45–57.

Digital Library

[30]

Taeho Jung, XuFei Mao, Xiang-Yang Li, Shao-Jie Tang, Wei Gong, and Lan Zhang. 2013. Privacy-preserving data aggregation without secure channel: Multivariate polynomial evaluation. In Proceedings of the IEEE INFOCOM Conference. IEEE, 2634–2642.

[31]

Jakub Konečnỳ, Brendan McMahan, and Daniel Ramage. 2015. Federated optimization: Distributed optimization beyond the datacenter. arXiv preprint arXiv:1511.03575 (2015).

[32]

Jakub Konečnỳ, H. Brendan McMahan, Felix X. Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon. 2016. Federated learning: Strategies for improving communication efficiency. arXiv preprint arXiv:1610.05492 (2016).

[33]

Srijan Kumar, Xikun Zhang, and Jure Leskovec. 2019. Predicting dynamic embedding trajectory in temporal interaction networks. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 1269–1278.

Digital Library

[34]

Jundong Li, Harsh Dani, Xia Hu, Jiliang Tang, Yi Chang, and Huan Liu. 2017. Attributed network embedding for learning in a dynamic environment. In Proceedings of the ACM Conference on Information and Knowledge Management. ACM, 387–396.

Digital Library

[35]

Jia Li, Zhichao Han, Hong Cheng, Jiao Su, Pengyun Wang, Jianfeng Zhang, and Lujia Pan. 2019. Predicting path failure in time-evolving graphs. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1279–1289.

Digital Library

[36]

Ruiyu Li, Makarand Tapaswi, Renjie Liao, Jiaya Jia, Raquel Urtasun, and Sanja Fidler. 2017. Situation recognition with graph neural networks. In Proceedings of the IEEE International Conference on Computer Vision. 4173–4182.

[37]

Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu. 2017. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv preprint arXiv:1707.01926 (2017).

[38]

Yehuda Lindell. 2017. How to simulate it-A tutorial on the simulation proof technique. In Tutorials on the Foundations of Cryptography. Springer, 277–346.

[39]

Jianxin Ma, Peng Cui, and Wenwu Zhu. 2018. DepthLGP: Learning embeddings of out-of-sample nodes in dynamic networks. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence.

[40]

Franco Manessi, Alessandro Rozza, and Mario Manzo. 2017. Dynamic graph convolutional networks. arXiv preprint arXiv:1704.06199 (2017).

[41]

H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, et al. 2016. Communication-efficient learning of deep networks from decentralized data. arXiv preprint arXiv:1602.05629 (2016).

[42]

Luca Melis, Congzheng Song, Emiliano De Cristofaro, and Vitaly Shmatikov. 2019. Exploiting unintended feature leakage in collaborative learning. In Proceedings of the IEEE Symposium on Security and Privacy (SP). IEEE, 691–706.

[43]

Payman Mohassel and Peter Rindal. 2018. ABY3: A mixed protocol framework for machine learning. In Proceedings of the ACM SIGSAC Conference on Computer and Communications Security. 35–52.

Digital Library

[44]

Milad Nasr, Reza Shokri, and Amir Houmansadr. 2019. Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning. In Proceedings of the IEEE Symposium on Security and Privacy (SP). IEEE, 739–753.

[45]

Giang Hoang Nguyen, John Boaz Lee, Ryan A. Rossi, Nesreen K. Ahmed, Eunyee Koh, and Sungchul Kim. 2018. Continuous-time dynamic network embeddings. In Proceedings of the the Web Conference. International World Wide Web Conferences Steering Committee, 969–976.

Digital Library

[46]

Mathias Niepert, Mohamed Ahmed, and Konstantin Kutzkov. 2016. Learning convolutional neural networks for graphs. In Proceedings of the International Conference on Machine Learning. 2014–2023.

[47]

Aldo Pareja, Giacomo Domeniconi, Jie Chen, Tengfei Ma, Toyotaro Suzumura, Hiroki Kanezashi, Tim Kaler, and Charles E. Leisersen. 2020. EvolveGCN: Evolving graph convolutional networks for dynamic graphs. In Proceedings of the AAAI Conference on Artificial Intelligence.

[48]

Hao Peng, Haoran Li, Yangqiu Song, Vincent Zheng, and Jianxin Li. 2021. Differentially private federated knowledge graphs embedding. In Proceedings of the 30th ACM International Conference on Information and Knowledge Management. ACM.

Digital Library

[49]

Meng Qu, Yoshua Bengio, and Jian Tang. 2019. GMNN: Graph Markov neural networks. In Proceedings of the 36th International Conference on Machine Learning.

[50]

Adam Richardson, Aris Filos-Ratsikas, and Boi Faltings. 2020. Budget-bounded incentives for federated learning. In Federated Learning. Springer, 176–188.

[51]

Alexandre Robicquet, Amir Sadeghian, Alexandre Alahi, and Silvio Savarese. 2016. Learning social etiquette: Human trajectory understanding in crowded scenes. In Proceedings of the European Conference on Computer Vision. Springer, 549–565.

[52]

Aravind Sankar, Yanhong Wu, Liang Gou, Wei Zhang, and Hao Yang. 2018. Dynamic graph representation learning via self-attention networks. arXiv preprint arXiv:1812.09430 (2018).

[53]

Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, and Max Welling. 2018. Modeling relational data with graph convolutional networks. In Proceedings of the European Semantic Web Conference. Springer, 593–607.

Digital Library

[54]

Youngjoo Seo, Michaël Defferrard, Pierre Vandergheynst, and Xavier Bresson. 2018. Structured sequence modeling with graph convolutional recurrent networks. In Proceedings of the International Conference on Neural Information Processing. Springer, 362–373.

Digital Library

[55]

Elaine Shi, T. H. Hubert Chan, Eleanor Rieffel, Richard Chow, and Dawn Song. 2011. Privacy-preserving aggregation of time-series data. In Proc. NDSS, Vol. 2. Citeseer, 1–17.

[56]

Yexuan Shi, Yongxin Tong, Zhiyang Su, Di Jiang, Zimu Zhou, and Wenbin Zhang. 2020. Federated topic discovery: A semantic consistent approach. IEEE Intell. Syst. 1, 1 (2020).

[57]

Lichao Sun, Yingtong Dou, Carl Yang, Ji Wang, Philip S. Yu, Lifang He, and Bo Li. 2018. Adversarial attack and defense on graph data: A survey. arXiv preprint arXiv:1812.10528 (2018).

[58]

Conghui Tan, Di Jiang, Huaxiao Mo, Jinhua Peng, Yongxin Tong, Weiwei Zhao, Chaotao Chen, Rongzhong Lian, Yuanfeng Song, and Qian Xu. 2020. Federated acoustic model optimization for automatic speech recognition. In Proceedings of the International Conference on Database Systems for Advanced Applications. Springer, 771–774.

Digital Library

[59]

Aleksei Triastcyn and Boi Faltings. 2019. Federated learning with bayesian differential privacy. In Proceedings of the IEEE International Conference on Big Data (Big Data). IEEE, 2587–2596.

[60]

Aleksei Triastcyn and Boi Faltings. 2020. Federated generative privacy. IEEE Intell. Syst. 35, 4 (2020), 50–57.

[61]

Stacey Truex, Ling Liu, Mehmet Emre Gursoy, Lei Yu, and Wenqi Wei. 2019. Demystifying membership inference attacks in machine learning as a service. IEEE Trans. Serv. Comput. 14, 6 (2019), 2073–2089.

[62]

Maria Valera and Sergio A. Velastin. 2005. Intelligent distributed surveillance systems: A review. Vis., Image Sig. Process. 152, 2 (2005), 192–204.

[63]

Daheng Wang, Meng Jiang, Munira Syed, Oliver Conway, Vishal Juneja, Sriram Subramanian, and Nitesh V. Chawla. 2020. Calendar graph neural networks for modeling time structures in spatiotemporal user behaviors. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2581–2589.

Digital Library

[64]

Yansheng Wang, Yongxin Tong, and Dingyuan Shi. 2020. Federated latent Dirichlet allocation: A local differential privacy based framework. In Proceedings of the AAAI Conference on Artificial Intelligence. 6283–6290.

[65]

Kang Wei, Jun Li, Ming Ding, Chuan Ma, Howard H. Yang, Farhad Farokhi, Shi Jin, Tony Q. S. Quek, and H. Vincent Poor. 2020. Federated learning with differential privacy: Algorithms and performance analysis. IEEE Trans. Inf. Forens. Secur. 15 (2020), 3454–3469. DOI:

Digital Library

[66]

Chuhan Wu, Fangzhao Wu, Yang Cao, Yongfeng Huang, and Xing Xie. 2021. FedGNN: Federated graph neural network for privacy-preserving recommendation. arXiv preprint arXiv:2102.04925 (2021).

[67]

Carl Yang, Haonan Wang, Ke Zhang, and Lichao Sun. 2021. Secure network release with link privacy. In Proceedings of the International Joint Conference on Artificial Intelligence.

[68]

Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong. 2019. Federated machine learning: Concept and applications. ACM Trans. Intell. Syst. Technol. 10, 2 (2019), 1–19.

Digital Library

[69]

Mehran Yazdi and Thierry Bouwmans. 2018. New trends on moving object detection in video images captured by a moving camera: A survey. Comput. Sci. Rev. 28 (2018), 157–177.

[70]

Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017).

[71]

Chengliang Zhang, Suyi Li, Junzhe Xia, Wei Wang, Feng Yan, and Yang Liu. 2020. BatchCrypt: Efficient homomorphic encryption for cross-silo federated learning. In Proceedings of the USENIX Annual Technical Conference (USENIX ATC’20). 493–506.

[72]

Tong Zhao, Chuchen Deng, Kaifeng Yu, Tianwen Jiang, Daheng Wang, and Meng Jiang. 2020. Error-bounded graph anomaly loss for GNNs. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 1873–1882.

Digital Library

[73]

Tong Zhao, Tianwen Jiang, Neil Shah, and Meng Jiang. 2021. A synergistic approach for graph anomaly detection with pattern mining and feature learning. IEEE Trans. Neural Netw. Learn. Syst. (2021). DOI:

[74]

Jun Zhou, Chaochao Chen, Longfei Zheng, Xiaolin Zheng, Bingzhe Wu, Ziqi Liu, and Li Wang. 2020. Privacy-preserving graph neural network for node classification. arXiv preprint arXiv:2005.11903 (2020).

[75]

Lekui Zhou, Yang Yang, Xiang Ren, Fei Wu, and Yueting Zhuang. 2018. Dynamic network embedding by modeling triadic closure process. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence.

[76]

Hangyu Zhu and Yaochu Jin. 2020. Multi-objective evolutionary federated learning. IEEE Trans. Neural Netw. Learn. Syst. 31, 4 (2020), 1310–1322.

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Index Terms

Federated Dynamic Graph Neural Networks with Secure Aggregation for Video-based Distributed Surveillance
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by regression
2. Security and privacy
  1. Cryptography
    1. Cryptanalysis and other attacks

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cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 13, Issue 4

August 2022

364 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3522732

Editor:
Huan Liu
Arizona State University, USA

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Publication History

Published: 03 May 2022

Online AM: 04 February 2022

Accepted: 01 November 2021

Revised: 01 July 2021

Received: 01 January 2021

Published in TIST Volume 13, Issue 4

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Zhang JTal I(2024)A Systematic Review of Contemporary Applications of Privacy-Aware Graph Neural Networks in Smart CitiesProceedings of the 19th International Conference on Availability, Reliability and Security10.1145/3664476.3669980(1-10)Online publication date: 30-Jul-2024
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Zhang HWu BYuan XPan STong HPei J(2024)Trustworthy Graph Neural Networks: Aspects, Methods, and TrendsProceedings of the IEEE10.1109/JPROC.2024.3369017112:2(97-139)Online publication date: Feb-2024
https://doi.org/10.1109/JPROC.2024.3369017
Hossain MImteaj AShahid A(2024)FedAVO: Improving Communication Efficiency in Federated Learning with African Vultures Optimizer2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC)10.1109/COMPSAC61105.2024.00069(455-462)Online publication date: 2-Jul-2024
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