ABSTRACT
The global deployment of the 5G network has led to a substantial increase in the deployment of edge servers to host web applications, catering to the growing demand for low service latency by edge web users. Yet, running edge servers 24/7 leads to enormous energy consumption and excessive carbon emissions. Energy-efficient edge resource provision is desired to achieve sustainable development goals in the new multi-access edge computing (MEC) architecture. Recently, several approaches have been proposed to solve the demand response problem for energy saving in cloud computing and MEC. However, accurate location information of edge web users should always be provided, which sacrifices users' privacy. To protect edge web users' location privacy while saving energy in MEC, we systematically formulate this location privacy-preserving edge demand response (LEDR) problem. To solve the LEDR problem effectively and efficiently, we propose a system named GEES by incorporating differential geo-obfuscation to secure user privacy while maximizing system utility and energy efficiency through inferences with theoretical analysis. Extensive and comprehensive experiments are conducted based on a synthetic real-world dataset, and the results demonstrate that GEES outperforms representative approaches by 23.02%, 31.47%, and 17.29% on average in terms of energy efficiency, user privacy and system utility.
Supplemental Material
- Anders SG Andrae and Tomas Edler. 2015. On global electricity usage of communication technology: trends to 2030. Challenges 6, 1 (2015), 117--157.Google ScholarCross Ref
- Miguel E Andrés, Nicolás E Bordenabe, Konstantinos Chatzikokolakis, and Catuscia Palamidessi. 2013. Geo-indistinguishability: Differential privacy for locationbased systems. In Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security. 901--914.Google ScholarDigital Library
- Shutong Chen, Lei Jiao, Fangming Liu, and Lin Wang. 2021. Edgedr: An online mechanism design for demand response in edge clouds. IEEE Transactions on Parallel and Distributed Systems 33, 2 (2021), 343--358.Google ScholarDigital Library
- Shutong Chen, Lei Jiao, Lin Wang, and Fangming Liu. 2019. An online market mechanism for edge emergency demand response via cloudlet control. In IEEE INFOCOM 2019-IEEE Conference on Computer Communications. IEEE, 2566--2574.Google ScholarDigital Library
- Zhipeng Cheng, Minghui Liwang, Xiaoyu Xia, Minghui Min, Xianbin Wang, and Xiaojiang Du. 2022. Auction-Promoted Trading for Multiple Federated Learning Services in UAV-Aided Networks. IEEE Transactions on Vehicular Technology 71, 10 (2022), 10960--10974.Google ScholarCross Ref
- Zhipeng Cheng, Xiaoyu Xia, Minghui Liwang, Xuwei Fan, Yanglong Sun, Xianbin Wang, and Lianfen Huang. 2023. CHEESE: Distributed Clustering-Based Hybrid Federated Split Learning Over Edge Networks. IEEE Transactions on Parallel and Distributed Systems 34, 12 (2023), 3174--3191. https://doi.org/10.1109/TPDS.2023. 3322755Google ScholarDigital Library
- Cory Cornelius, Apu Kapadia, David Kotz, Dan Peebles, Minho Shin, and Nikos Triandopoulos. 2008. Anonysense: privacy-aware people-centric sensing. In Proceedings of the 6th international conference on Mobile systems, applications, and services. 211--224.Google ScholarDigital Library
- Guangming Cui, Qiang He, Xiaoyu Xia, Feifei Chen, Tao Gu, Hai Jin, and Yun Yang. 2021. Demand response in noma-based mobile edge computing: A twophase game-theoretical approach. IEEE Transactions on Mobile Computing (2021).Google Scholar
- Guangming Cui, Qiang He, Xiaoyu Xia, Feifei Chen, and Yun Yang. 2023. EESaver: Saving Energy Dynamically for Green Multi-access Edge Computing. IEEE Transactions on Parallel and Distributed Systems (2023).Google Scholar
- Bharath Bhushan Damodaran, Benjamin Kellenberger, Rémi Flamary, Devis Tuia, and Nicolas Courty. 2018. Deepjdot: Deep joint distribution optimal transport for unsupervised domain adaptation. In Proceedings of the European conference on computer vision (ECCV). 447--463.Google ScholarDigital Library
- Christos Dimitrakakis, Blaine Nelson, Zuhe Zhang, Aikateirni Mitrokotsa, and Benjamin IP Rubinstein. 2017. Differential privacy for Bayesian inference through posterior sampling. Journal of machine learning research 18, 11 (2017), 1--39.Google Scholar
- Khoa D Doan, Peng Yang, and Ping Li. 2022. One loss for quantization: Deep hashing with discrete wasserstein distributional matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 9447--9457.Google ScholarCross Ref
- Cynthia Dwork. 2006. Differential privacy. In International colloquium on automata, languages, and programming. Springer, 1--12.Google Scholar
- Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith. 2006. Calibrating noise to sensitivity in private data analysis. In Theory of Cryptography: Third Theory of Cryptography Conference, TCC 2006, New York, NY, USA, March 4--7, 2006. Proceedings 3. Springer, 265--284.Google ScholarDigital Library
- Cynthia Dwork, Aaron Roth, et al. 2014. The algorithmic foundations of differential privacy. Foundations and Trends® in Theoretical Computer Science 9, 3--4 (2014), 211--407.Google Scholar
- Liyue Fan, Luca Bonomi, Li Xiong, and Vaidy Sunderam. 2014. Monitoring web browsing behavior with differential privacy. In Proceedings of the 23rd international conference on World wide web. 177--188.Google ScholarDigital Library
- Xiaohu Ge, Song Tu, Guoqiang Mao, Cheng-Xiang Wang, and Tao Han. 2016. 5G ultra-dense cellular networks. IEEE Wireless Communications 23, 1 (2016), 72--79.Google ScholarCross Ref
- Nicola Jones et al. 2018. How to stop data centres from gobbling up the world's electricity. Nature 561, 7722 (2018), 163--166.Google Scholar
- Charlotte Laclau, Ievgen Redko, Basarab Matei, Younes Bennani, and Vincent Brault. 2017. Co-clustering through optimal transport. In International conference on machine learning. PMLR, 1955--1964.Google Scholar
- Phu Lai, Qiang He, Mohamed Abdelrazek, Feifei Chen, John Hosking, John Grundy, and Yun Yang. 2018. Optimal edge user allocation in edge computing with variable sized vector bin packing. In Service-Oriented Computing: 16th International Conference, ICSOC 2018, Hangzhou, China, November 12--15, 2018, Proceedings 16. Springer, 230--245.Google ScholarDigital Library
- An Liu, Xindi Shen, Zhixu Li, Guanfeng Liu, Jiajie Xu, Lei Zhao, Kai Zheng, and Shuo Shang. 2019. Differential private collaborativeWeb services QoS prediction. World Wide Web 22 (2019), 2697--2720.Google ScholarCross Ref
- Wenbin Liu, Yongjian Yang, En Wang, and Jie Wu. 2020. Dynamic user recruitment with truthful pricing for mobile crowdsensing. In IEEE INFOCOM 2020-IEEE Conference on Computer Communications. IEEE, 1113--1122.Google ScholarDigital Library
- Quyuan Luo, Shihong Hu, Changle Li, Guanghui Li, and Weisong Shi. 2021. Resource scheduling in edge computing: A survey. IEEE Communications Surveys & Tutorials 23, 4 (2021), 2131--2165.Google ScholarCross Ref
- Saerom Park, Junyoung Byun, and Joohee Lee. 2022. Privacy-preserving fair learning of support vector machine with homomorphic encryption. In Proceedings of the ACM Web Conference 2022. 3572--3583.Google ScholarDigital Library
- Junkun Peng, Qing Li, Xiaoteng Ma, Yong Jiang, Yutao Dong, Chuang Hu, and Meng Chen. 2022. MagNet: cooperative edge caching by automatic content congregating. In Proceedings of the ACM Web Conference 2022. 3280--3288.Google ScholarDigital Library
- Gabriel Peyré, Marco Cuturi, et al. 2019. Computational optimal transport: With applications to data science. Foundations and Trends® in Machine Learning 11, 5--6 (2019), 355--607.Google Scholar
- Riccardo Poli, James Kennedy, and Tim Blackwell. 2007. Particle swarm optimization: An overview. Swarm intelligence 1 (2007), 33--57.Google Scholar
- Layla Pournajaf, Li Xiong, Vaidy Sunderam, and Slawomir Goryczka. 2014. Spatial task assignment for crowd sensing with cloaked locations. In 2014 IEEE 15th International Conference on Mobile Data Management, Vol. 1. IEEE, 73--82.Google ScholarDigital Library
- Houyi Qi, Minghui Liwang, Seyyedali Hosseinalipour, Xiaoyu Xia, Zhipeng Cheng, Xianbin Wang, and Zhenzhen Jiao. 2023. Matching-Based Hybrid Service Trading for Task Assignment Over Dynamic Mobile Crowdsensing Networks. IEEE Transactions on Services Computing (2023), 1--14. https://doi.org/10.1109/ TSC.2023.3333832Google Scholar
- Bilal Shebaro, Salmin Sultana, Shakthidhar Reddy Gopavaram, and Elisa Bertino. 2012. Demonstrating a lightweight data provenance for sensor networks. In Proceedings of the 2012 ACM conference on Computer and communications security. 1022--1024.Google ScholarDigital Library
- Reza Shokri. 2014. Privacy games: Optimal user-centric data obfuscation. arXiv preprint arXiv:1402.3426 (2014).Google Scholar
- Tarik Taleb, Konstantinos Samdanis, Badr Mada, Hannu Flinck, Sunny Dutta, and Dario Sabella. 2017. On multi-access edge computing: A survey of the emerging 5G network edge cloud architecture and orchestration. IEEE Communications Surveys & Tutorials 19, 3 (2017), 1657--1681.Google ScholarDigital Library
- Cédric Villani et al. 2009. Optimal transport: old and new. Vol. 338. Springer.Google Scholar
- FeiWang, Lei Jiao, Konglin Zhu, Xiaojun Lin, and Lei Li. 2023. Toward Sustainable AI: Federated Learning Demand Response in Cloud-Edge Systems via Auctions. In IEEE INFOCOM 2023-IEEE Conference on Computer Communications. IEEE, 1--10.Google Scholar
- Kaibin Wang, Qiang He, Feifei Chen, Hai Jin, and Yun Yang. 2023. FedEdge: Accelerating Edge-Assisted Federated Learning. In Proceedings of the ACM Web Conference 2023. 2895--2904.Google ScholarDigital Library
- LeyeWang, Dingqi Yang, Xiao Han, TianbenWang, Daqing Zhang, and Xiaojuan Ma. 2017. Location privacy-preserving task allocation for mobile crowdsensing with differential geo-obfuscation. In Proceedings of the 26th International Conference on World Wide Web. 627--636.Google Scholar
- Leon Willenborg and Ton De Waal. 2012. Elements of statistical disclosure control. Vol. 155. Springer Science & Business Media.Google Scholar
- Xiaoyu Xia, Feifei Chen, John Grundy, Mohamed Abdelrazek, Hai Jin, and Qiang He. 2021. Constrained app data caching over edge server graphs in edge computing environment. IEEE Transactions on Services Computing 15, 5 (2021), 2635-- 2647.Google ScholarCross Ref
- Xiaoyu Xia, Feifei Chen, Qiang He, Guangming Cui, John Grundy, Mohamed Abdelrazek, Athman Bouguettaya, and Hai Jin. 2021. OL-MEDC: An online approach for cost-effective data caching in mobile edge computing systems. IEEE Transactions on Mobile Computing (2021).Google Scholar
- Xiaoyu Xia, Feifei Chen, Qiang He, Guangming Cui, John C Grundy, Mohamed Abdelrazek, Xiaolong Xu, and Hai Jin. 2021. Data, user and power allocations for caching in multi-access edge computing. IEEE Transactions on Parallel and Distributed Systems 33, 5 (2021), 1144--1155.Google ScholarDigital Library
- Xiaoyu Xia, Feifei Chen, Qiang He, John Grundy, Mohamed Abdelrazek, Jun Shen, Athman Bouguettaya, and Hai Jin. 2022. Formulating cost-effective data distribution strategies online for edge cache systems. IEEE Transactions on Parallel and Distributed Systems 33, 12 (2022), 4270--4281.Google ScholarDigital Library
- Xiaoyu Xia, Sheik Mohammad Mostakim Fattah, and Muhammad Ali Babar. 2023. A survey on UAV-enabled edge computing: Resource management perspective. Comput. Surveys 56, 3 (2023), 1--36.Google ScholarDigital Library
- Huizi Xiao, Qingyang Zhang, Qingqi Pei, and Weisong Shi. 2021. Privacypreserving neural network inference framework via homomorphic encryption and sgx. In 2021 IEEE 41st International Conference on Distributed Computing Systems (ICDCS). IEEE, 751--761.Google Scholar
- Yonghui Xiao and Li Xiong. 2015. Protecting locations with differential privacy under temporal correlations. In Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security. 1298--1309.Google ScholarDigital Library
- Dianlei Xu, Tong Li, Yong Li, Xiang Su, Sasu Tarkoma, Tao Jiang, Jon Crowcroft, and Pan Hui. 2021. Edge intelligence: Empowering intelligence to the edge of network. Proc. IEEE 109, 11 (2021), 1778--1837.Google ScholarCross Ref
- Xun Yi, Russell Paulet, and Elisa Bertino. 2014. Homomorphic encryption. Springer.Google Scholar
- Xuefei Yin, Yanming Zhu, and Jiankun Hu. 2021. A comprehensive survey of privacy-preserving federated learning: A taxonomy, review, and future directions. ACM Computing Surveys (CSUR) 54, 6 (2021), 1--36.Google ScholarDigital Library
- Lei Yu, Ling Liu, and Calton Pu. 2017. Dynamic Differential Location Privacy with Personalized Error Bounds.. In NDSS.Google Scholar
- Guoxing Zhan, Weisong Shi, and Julia Deng. 2011. Design and implementation of TARF: A trust-aware routing framework for WSNs. IEEE Transactions on dependable and secure computing 9, 2 (2011), 184--197.Google ScholarDigital Library
- Chuan Zhang, Mingyang Zhao, Liehuang Zhu, Tong Wu, and Ximeng Liu. 2022. Enabling efficient and strong privacy-preserving truth discovery in mobile crowdsensing. IEEE Transactions on Information Forensics and Security 17 (2022), 3569-- 3581.Google ScholarCross Ref
- Letian Zhang, Lixing Chen, and Jie Xu. 2021. Autodidactic neurosurgeon: Collaborative deep inference for mobile edge intelligence via online learning. In Proceedings of the Web Conference 2021. 3111--3123.Google ScholarDigital Library
- Linquan Zhang, Shaolei Ren, Chuan Wu, and Zongpeng Li. 2015. A truthful incentive mechanism for emergency demand response in colocation data centers. In 2015 IEEE Conference on Computer Communications (INFOCOM). IEEE, 2632-- 2640.Google ScholarCross Ref
- Xu Zheng, Zhipeng Cai, Jianzhong Li, and Hong Gao. 2017. Location-privacyaware review publication mechanism for local business service systems. In IEEE INFOCOM 2017-IEEE Conference on Computer Communications. IEEE, 1--9.Google ScholarCross Ref
- Zhi Zhou, Fangming Liu, Zongpeng Li, and Hai Jin. 2015. When smart grid meets geo-distributed cloud: An auction approach to datacenter demand response. In 2015 IEEE Conference on Computer Communications (INFOCOM). IEEE, 2650--2658.Google ScholarCross Ref
- Agustin Zuniga, Huber Flores, Eemil Lagerspetz, Petteri Nurmi, Sasu Tarkoma, Pan Hui, and Jukka Manner. 2019. Tortoise or hare? quantifying the effects of performance on mobile app retention. In The World Wide Web Conference. 2517--2528.Google ScholarDigital Library
Index Terms
- GEES: Enabling Location Privacy-Preserving Energy Saving in Multi-Access Edge Computing
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