A privacy-preserving access control scheme with verifiable and outsourcing capabilities in fog-cloud computing

Paper

Cheng, Zhen, Zhang, Jiale, Qian, Hongyan, Xiang, Mingrong and Wu, Di. 2019. "A privacy-preserving access control scheme with verifiable and outsourcing capabilities in fog-cloud computing." 19th International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP 2019). Melbourne, Australia 09 - 11 Dec 2019 Switzerland . Springer. https://doi.org/10.1007/978-3-030-38991-8_23
Paper/Presentation Title

A privacy-preserving access control scheme with verifiable and outsourcing capabilities in fog-cloud computing

Presentation TypePaper
AuthorsCheng, Zhen, Zhang, Jiale, Qian, Hongyan, Xiang, Mingrong and Wu, Di
Journal or Proceedings TitleProceedings of the 19th International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP 2019)
Journal Citation11944, pp. 345-358
Number of Pages14
Year2019
PublisherSpringer
Place of PublicationSwitzerland
ISBN9783030389901
9783030389918
Digital Object Identifier (DOI)https://doi.org/10.1007/978-3-030-38991-8_23
Web Address (URL) of Paperhttps://link.springer.com/chapter/10.1007/978-3-030-38991-8_23
Web Address (URL) of Conference Proceedingshttps://link.springer.com/book/10.1007/978-3-030-38991-8
Conference/Event19th International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP 2019)
Event Details
19th International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP 2019)
Parent
International Conference on Algorithms and Architectures for Parallel Processing
Delivery
In person
Event Date
09 to end of 11 Dec 2019
Event Location
Melbourne, Australia
Rank
B
Abstract

Fog computing is a distribution system architecture which uses edge devices to provide computation, storage, and sharing at the edge of the network as an extension of cloud computing architecture, where the potential network traffic jams can be resolved. Whereas, the untrustworthy edge devices which contribute the computing resources may lead to data security and privacy-preserving issues. To address security issues and achieve fine-grained access control to protect privacy of users, ciphertext-policy attribute-based encryption (CP-ABE) mechanism has been well-explored, where data owners obtain flexible access policy to share data between users. However, the major drawback of CP-ABE system is heavy computational cost due to the complicated cryptographic operations. To tackle this problem, we propose a privacy-preserving access control (PPAC) scheme and the contributions are tri-folded: (1) we introduce outsourcing capability in fog-cloud computing (FCC) environment; (2) the outsource verification mechanism has been considered to guarantee the third party execute the algorithm correctly; (3) we design a partiality hidden method to protect the privacy information embedded in the access structures. The experimental results show that our proposed PPAC is efficient, economical and suitable for mobile devices with limited resources.

KeywordsFog-cloud computing; Attribute-based encryption; Privacy- preserving; Access Control
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020460603. Cyberphysical systems and internet of things
Public Notes

Files associated with this item cannot be displayed due to copyright restrictions.

Byline AffiliationsNanjing University of Aeronautics and Astronautics, China
Deakin University
University of Technology Sydney
Permalink -

https://research.usq.edu.au/item/z4y1w/a-privacy-preserving-access-control-scheme-with-verifiable-and-outsourcing-capabilities-in-fog-cloud-computing

Log in to edit
  • 2
    total views
  • 1
    total downloads
  • 1
    views this month
  • 0
    downloads this month

Export as

Related outputs

BADFSS: Backdoor Attacks on Federated Self-Supervised Learning
Zhang, Jiale, Zhu, Chengcheng, Wu, Di, Sun, Xiaobing, Yong, Jianming and Long, Guodong. 2024. "BADFSS: Backdoor Attacks on Federated Self-Supervised Learning." Larson, Kate (ed.) 33rd International Joint Conference on Artificial Intelligence (IJCAI-24). Jeju, Korea 03 - 09 Aug 2024 Korea. https://doi.org/10.24963/ijcai.2024/61
From wide to deep: dimension lifting network for parameter-efficient knowledge graph embedding
Cai, Borui, Xiang, Yong, Gao, Longxiang, Wu, Di, Zhang, He, Jin, Jiong and Luan, Tom. 2024. "From wide to deep: dimension lifting network for parameter-efficient knowledge graph embedding." IEEE Transactions on Knowledge and Data Engineering. https://doi.org/DOI:10.1109/TKDE.2024.3437479
EXVUL: Towards Effective and Explainable Vulnerability Detection for IoT Devices
Cao, Sicong, Sun, Xiaobing, Liu, Wei, Wu, Di, Zhang, Jiale, Li, Yan, Luan, Tom H. and Gao, Longxiang. 2024. "EXVUL: Towards Effective and Explainable Vulnerability Detection for IoT Devices." IEEE Internet of Things Journal. 11 (12), pp. 22385-22398. https://doi.org/10.1109/JIOT.2024.3381641
Robust equivalent circuit model parameters identification scheme for State of Charge (SOC) estimation based on maximum correntropy criterion
Zhang, Kexin, Zhao, Xuezhuan, Chen, Yu, Wu, Di, Cai, Taotao, Wang, Yi, Li, Lingling and Zhang, Ji. 2024. "Robust equivalent circuit model parameters identification scheme for State of Charge (SOC) estimation based on maximum correntropy criterion." International Journal of Electrochemical Science. 19 (5). https://doi.org/10.1016/j.ijoes.2024.100558
FedInverse: Evaluating Privacy Leakage in Federated Learning
Wu, Di, Bai, Jun, Song,Yiliao, Chen, Junjun, Zhou, Wei, Xiang, Yong and Sajjanhar, Atul. 2024. "FedInverse: Evaluating Privacy Leakage in Federated Learning." The Twelfth International Conference on Learning Representations. Vienna, Austria 07 - 11 May 2024 Austria.
Privacy Inference Attack and Defense in Centralized and Federated Learning: A Comprehensive Survey
Rao, Bosen, Zhang, Jiale, Wu, Di, Zhu, Chengcheng, Sun, Xiaobing and Chen, Bing. 2024. "Privacy Inference Attack and Defense in Centralized and Federated Learning: A Comprehensive Survey." IEEE Transactions on Artificial Intelligence. https://doi.org/10.1109/TAI.2024.3363670
VPFL: A verifiable privacy-preserving federated learning scheme for edge computing systems
Zhang, Jiale, Liu, Yue, Wu, Di, Lou, Shuai, Chen, Bing and Yu, Shui. 2023. "VPFL: A verifiable privacy-preserving federated learning scheme for edge computing systems." Digital Communications and Networks. 9 (4), pp. 981-989. https://doi.org/10.1016/j.dcan.2022.05.010
Hybrid KD-NFT: A multi-layered NFT assisted robust Knowledge Distillation framework for Internet of Things
Wang, Nai, Chen, Junjun, Wu, Di, Yang, Wencheng, Xiang, Yong and Sajjanhar, Atul. 2023. "Hybrid KD-NFT: A multi-layered NFT assisted robust Knowledge Distillation framework for Internet of Things." Journal of Information Security and Applications. 75. https://doi.org/10.1016/j.jisa.2023.103483
Defending against membership inference attacks in federated learning via adversarial example
Xie, Yuanyuan, Chen, Bing, Zhang, Jiale and Wu, Di. 2021. "Defending against membership inference attacks in federated learning via adversarial example." 2021 17th International Conference on Mobility, Sensing and Networking (MSN). Exeter, United Kingdom 13 - 15 Dec 2021 United States. IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/MSN53354.2021.00036
Campus Network Intrusion Detection based on Federated Learning
Chen, Junjun, Guo, Qiang, Fu, Zhongnan, Shang, Qun, Ma, Hao and Wu, Di. 2022. "Campus Network Intrusion Detection based on Federated Learning." 2022 International Joint Conference on Neural Networks (IJCNN). Padua, Italy 18 - 23 Jul 2022 United States. IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/IJCNN55064.2022.9892843
From distributed machine learning to federated learning: In the view of data privacy and security
Shen, Sheng, Zhu, Tianqing, Wu, Di, Wang, Wei and Zhou, Wanlei. 2022. "From distributed machine learning to federated learning: In the view of data privacy and security." Concurrency and Computation: Practice and Experience. 34 (16). https://doi.org/10.1002/cpe.6002
A Blockchain-based Multi-layer Decentralized Framework for Robust Federated Learning
Wu, Di, Wang, Nai, Zhang, Jiale, Zhang, Yuan, Xiang, Yong and Gao, Longxiang. 2022. "A Blockchain-based Multi-layer Decentralized Framework for Robust Federated Learning." 2022 International Joint Conference on Neural Networks (IJCNN). Padua, Italy 18 - 23 Jul 2022 IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/IJCNN55064.2022.9892039
Detecting and mitigating poisoning attacks in federated learning using generative adversarial networks
Zhao, Ying, Chen, Junjun, Zhang, Jiale, Wu, Di, Blumenstein, Michael and Yu, Shui. 2022. "Detecting and mitigating poisoning attacks in federated learning using generative adversarial networks." Concurrency and Computation: Practice and Experience. 34 (7). https://doi.org/10.1002/cpe.5906
Defending poisoning attacks in federated learning via adversarial training method
Zhang, Jiale, Wu, Di, Liu, Chengyong and Chen, Bing. 2020. "Defending poisoning attacks in federated learning via adversarial training method." 3rd International Conference on Frontiers in Cyber Security (FCS 2020). Tianjin, China 15 - 17 Nov 2020 Singapore . Springer. https://doi.org/10.1007/978-981-15-9739-8_7
An End-to-End Hierarchical Classification Approach for Similar Gesture Recognition
Wu, Di, Sharma, Nabin and Blumenstein, Michael. 2019. "An End-to-End Hierarchical Classification Approach for Similar Gesture Recognition." 2018 International Conference on Image and Vision Computing New Zealand (IVCNZ). Auckland, New Zealand 19 - 21 Nov 2018 United States. IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/IVCNZ.2018.8634660
Similar Gesture Recognition using Hierarchical Classification Approach in RGB Videos
Wu, Di, Sharma, Nabin and Blumenstein, Michael. 2019. "Similar Gesture Recognition using Hierarchical Classification Approach in RGB Videos." 2018 Digital Image Computing: Techniques and Applications (DICTA). Canberra, Australia 10 - 13 Dec 2018 United States. IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/DICTA.2018.8615804
Adversarial action data augmentation for similar gesture action recognition
Wu, Di, Chen, Junjun, Sharma, Nabin, Pan, Shirui, Long, Guodong and Blumenstein, Michael. 2019. "Adversarial action data augmentation for similar gesture action recognition." 2019 International Joint Conference on Neural Networks (IJCNN). Budapest, Hungary 14 - 19 Jul 2019 United States. IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/IJCNN.2019.8851993
Feature-dependent graph convolutional autoencoders with adversarial training methods
Wu, Di, Hu, Ruiqi, Zheng, Yu, Jiang, Jing, Sharma, Nabin and Blumenstein, Michael. 2019. "Feature-dependent graph convolutional autoencoders with adversarial training methods." 2019 International Joint Conference on Neural Networks (IJCNN). Budapest, Hungary 14 - 19 Jul 2019 United States. IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/IJCNN.2019.8852314
Poisoning attack in federated learning using generative adversarial nets
Zhang, Jiale, Chen, Junjun, Wu, Di, Chen, Bing and Yu, Shui. 2019. "Poisoning attack in federated learning using generative adversarial nets." 2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). Rotorua, New Zealand 05 - 08 Aug 2018 United States. IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/TrustCom/BigDataSE.2019.00057
Network anomaly detection by using a time-decay closed frequent pattern
Zhao, Ying, Chen, Junjun, Wu, Di, Teng, Jian, Sharma, Nabin, Sajjanhar, Atul and Blumenstein, Michael. 2019. "Network anomaly detection by using a time-decay closed frequent pattern." Information (Basel). 10 (8). https://doi.org/10.3390/info10080262
Multi-task network anomaly detection using federated learning
Zhao, Ying, Chen, Junjun, Wu, Di, Teng, Jian and Yu, Shui. 2019. "Multi-task network anomaly detection using federated learning." 10th international symposium on information and communication technology (SoICT 2019). Hanoi, Viet Nam 04 - 06 Dec 2019 United States. Association for Computing Machinery (ACM). https://doi.org/10.1145/3368926.3369705
Robust feature-based automated multi-view human action recognition system
Chou, Kuang-Pen, Prasad, Mukesh, Wu, Di, Sharma, Nabin, Li, Dong-Lin, Lin, Yu-Feng, Blumenstein, Michael, Lin, Wen-Chieh and Lin, Chin-Teng. 2018. "Robust feature-based automated multi-view human action recognition system." IEEE Access. 6, pp. 15283-15296. https://doi.org/10.1109/ACCESS.2018.2809552
Recent advances in video-based human action recognition using deep learning: A review
Wu, Di, Sharma, Nabin and Blumenstein, Michael. 2017. "Recent advances in video-based human action recognition using deep learning: A review." 2017 International Joint Conference on Neural Networks (IJCNN). Anchorage, United States 14 - 19 May 2017 United States. IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/IJCNN.2017.7966210
On addressing the imbalance problem: a correlated KNN approach for network traffic classification
Wu, Di, Chen, Xiao, Chen, Chao, Zhang, Jun, Xiang, Yang and Zhou, Wanlei. 2015. "On addressing the imbalance problem: a correlated KNN approach for network traffic classification." NSS 2014: 8th International Conference on Network and System Security. Xi'an, China 15 - 17 Oct 2014 Switzerland . Springer. https://doi.org/10.1007/978-3-319-11698-3_11
Detecting stepping stones by abnormal causality probability
Wen, Sheng, Wu, Di, Li, Ping, Xiang, Yang, Zhou, Wanlei and Wei, Guiyi. 2015. "Detecting stepping stones by abnormal causality probability." Security and Communication Networks. 8 (10), pp. 1831-1844. https://doi.org/10.1002/sec.1037
A Survey on Latest Botnet Attack and Defense
Zhang, Lei, Yu, Shui, Wu, Di and Watters, Paul. 2011. "A Survey on Latest Botnet Attack and Defense ." 10th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom 2011). Changsha, China 16 - 18 Nov 2011 China. https://doi.org/10.1109/TrustCom.2011.11