{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T03:39:35Z","timestamp":1773113975122,"version":"3.50.1"},"reference-count":32,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,11,16]],"date-time":"2023-11-16T00:00:00Z","timestamp":1700092800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2020YFB1806607"],"award-info":[{"award-number":["2020YFB1806607"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFB2902204"],"award-info":[{"award-number":["2022YFB2902204"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Data sharing and analyzing among different devices in mobile edge computing is valuable for social innovation and development. The limitation to the achievement of this goal is the data privacy risk. Therefore, existing studies mainly focus on enhancing the data privacy-protection capability. On the one hand, direct data leakage is avoided through federated learning by converting raw data into model parameters for transmission. On the other hand, the security of federated learning is further strengthened by privacy-protection techniques to defend against inference attack. However, privacy-protection techniques may reduce the training accuracy of the data while improving the security. Particularly, trading off data security and accuracy is a major challenge in dynamic mobile edge computing scenarios. To address this issue, we propose a federated-learning-based privacy-protection scheme, FLPP. Then, we build a layered adaptive differential privacy model to dynamically adjust the privacy-protection level in different situations. Finally, we design a differential evolutionary algorithm to derive the most suitable privacy-protection policy for achieving the optimal overall performance. The simulation results show that FLPP has an advantage of 8\u223c34% in overall performance. This demonstrates that our scheme can enable data to be shared securely and accurately.<\/jats:p>","DOI":"10.3390\/e25111551","type":"journal-article","created":{"date-parts":[[2023,11,17]],"date-time":"2023-11-17T00:49:47Z","timestamp":1700182187000},"page":"1551","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["FLPP: A Federated-Learning-Based Scheme for Privacy Protection in Mobile Edge Computing"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7074-6228","authenticated-orcid":false,"given":"Zhimo","family":"Cheng","sequence":"first","affiliation":[{"name":"Department of Next-Generation Mobile Communication and Cyber Space Security, Information Engineering University, Zhengzhou 450002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinsheng","family":"Ji","sequence":"additional","affiliation":[{"name":"Department of Next-Generation Mobile Communication and Cyber Space Security, Information Engineering University, Zhengzhou 450002, China"},{"name":"Purple Mountain Laboratories, Nanjing 211111, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"You","sequence":"additional","affiliation":[{"name":"Department of Next-Generation Mobile Communication and Cyber Space Security, Information Engineering University, Zhengzhou 450002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Bai","sequence":"additional","affiliation":[{"name":"Department of Next-Generation Mobile Communication and Cyber Space Security, Information Engineering University, Zhengzhou 450002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunjie","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Next-Generation Mobile Communication and Cyber Space Security, Information Engineering University, Zhengzhou 450002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaogang","family":"Qin","sequence":"additional","affiliation":[{"name":"Department of Next-Generation Mobile Communication and Cyber Space Security, Information Engineering University, Zhengzhou 450002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MCOM.2016.1600492CM","article-title":"EdgeIoT: Mobile Edge Computing for the Internet of Things","volume":"54","author":"Sun","year":"2016","journal-title":"IEEE Commun. Mag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"85714","DOI":"10.1109\/ACCESS.2020.2991734","article-title":"An Overview on Edge Computing Research","volume":"8","author":"Cao","year":"2020","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2462","DOI":"10.1109\/COMST.2020.3009103","article-title":"Edge Computing in Industrial Internet of Things: Architecture, Advances and Challenges","volume":"22","author":"Qiu","year":"2020","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_4","unstructured":"Lee, W., and Leung, C.K. (2017, January 15\u201318). Constrained Big Data Mining in an Edge Computing Environment. Proceedings of the Big Data Applications and Services 2017, Tashkent, Uzbekistan."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/MCOM.2018.1701148","article-title":"Big Data Privacy Preserving in Multi-Access Edge Computing for Heterogeneous Internet of Things","volume":"56","author":"Du","year":"2018","journal-title":"IEEE Commun. Mag."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3339474","article-title":"Federated Machine Learning: Concept and Applications","volume":"10","author":"Yang","year":"2019","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_7","first-page":"1273","article-title":"Communication-Efficient Learning of Deep Networks from Decentralized Data","volume":"Volume 54","author":"Singh","year":"2017","journal-title":"Proceedings of the 20th International Conference on Artificial Intelligence and Statistics"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MCE.2019.2959108","article-title":"Preserving Data Privacy via Federated Learning: Challenges and Solutions","volume":"9","author":"Li","year":"2020","journal-title":"IEEE Consum. Electron. Mag."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1016\/j.future.2020.10.007","article-title":"A survey on security and privacy of federated learning","volume":"115","author":"Mothukuri","year":"2021","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_10","unstructured":"Zhu, L., Liu, Z., and Han, S. (2019). Deep leakage from gradients. Adv. Neural Inf. Process. Syst., 32."},{"key":"ref_11","first-page":"50","article-title":"Federated Learning: Challenges, Methods, and Future Directions","volume":"37","author":"Li","year":"2020","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Fang, H., and Qian, Q. (2021). Privacy preserving machine learning with homomorphic encryption and federated learning. Future Internet, 13.","DOI":"10.3390\/fi13040094"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"18364","DOI":"10.1109\/JIOT.2023.3279830","article-title":"Privacy-Preserving Federal Learning Chain for Internet of Things","volume":"10","author":"Xu","year":"2023","journal-title":"IEEE Internet Things J."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhang, J., Chen, B., Yu, S., and Deng, H. (2019, January 9\u201313). PEFL: A Privacy-Enhanced Federated Learning Scheme for Big Data Analytics. Proceedings of the 2019 IEEE Global Communications Conference (GLOBECOM), Waikoloa, HI, USA.","DOI":"10.1109\/GLOBECOM38437.2019.9014272"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1109\/MNET.007.2100717","article-title":"SMPC-Based Federated Learning for 6G-Enabled Internet of Medical Things","volume":"36","author":"Kalapaaking","year":"2022","journal-title":"IEEE Netw."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1985","DOI":"10.1109\/TNSE.2023.3237367","article-title":"Mitfed: A privacy preserving collaborative network attack mitigation framework based on federated learning using sdn and blockchain","volume":"10","author":"Hafid","year":"2023","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Sotthiwat, E., Zhen, L., Li, Z., and Zhang, C. (2021, January 10\u201313). Partially Encrypted Multi-Party Computation for Federated Learning. Proceedings of the 2021 IEEE\/ACM 21st International Symposium on Cluster, Cloud and Internet Computing (CCGrid), Melbourne, Australia.","DOI":"10.1109\/CCGrid51090.2021.00101"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Fereidooni, H., Marchal, S., Miettinen, M., Mirhoseini, A., M\u00f6llering, H., Nguyen, T.D., Rieger, P., Sadeghi, A.R., Schneider, T., and Yalame, H. (2021, January 27). SAFELearn: Secure Aggregation for private FEderated Learning. Proceedings of the 2021 IEEE Security and Privacy Workshops (SPW), San Francisco, CA, USA.","DOI":"10.1109\/SPW53761.2021.00017"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Galletta, A., Taheri, J., Celesti, A., Fazio, M., and Villari, M. (2023). Investigating the Applicability of Nested Secret Share for Drone Fleet Photo Storage. IEEE Trans. Mob. Comput., 1\u201313.","DOI":"10.1109\/TMC.2023.3263115"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Galletta, A., Taheri, J., and Villari, M. (2019, January 14\u201317). On the Applicability of Secret Share Algorithms for Saving Data on IoT, Edge and Cloud Devices. Proceedings of the 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), Atlanta, GA, USA.","DOI":"10.1109\/iThings\/GreenCom\/CPSCom\/SmartData.2019.00026"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Galletta, A., Taheri, J., Fazio, M., Celesti, A., and Villari, M. (2021, January 20\u201322). Overcoming security limitations of Secret Share techniques: The Nested Secret Share. Proceedings of the 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), Shenyang, China.","DOI":"10.1109\/TrustCom53373.2021.00054"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1007\/s42045-020-00045-8","article-title":"A trusted recommendation scheme for privacy protection based on federated learning","volume":"3","author":"Wang","year":"2020","journal-title":"CCF Trans. Netw."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3454","DOI":"10.1109\/TIFS.2020.2988575","article-title":"Federated Learning with Differential Privacy: Algorithms and Performance Analysis","volume":"15","author":"Wei","year":"2020","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"6314","DOI":"10.1109\/TII.2021.3052183","article-title":"Anonymous and Privacy-Preserving Federated Learning With Industrial Big Data","volume":"17","author":"Zhao","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1953","DOI":"10.1038\/s41598-022-05539-7","article-title":"Federated learning and differential privacy for medical image analysis","volume":"12","author":"Adnan","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"18706","DOI":"10.1109\/ACCESS.2021.3053233","article-title":"Multi-Access Edge Computing Architecture, Data Security and Privacy: A Review","volume":"9","author":"Ali","year":"2021","journal-title":"IEEE Access"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3347","DOI":"10.1109\/TKDE.2021.3124599","article-title":"A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection","volume":"35","author":"Li","year":"2023","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Melis, L., Song, C., De Cristofaro, E., and Shmatikov, V. (2019, January 20\u201322). Exploiting unintended feature leakage in collaborative learning. Proceedings of the 2019 IEEE Symposium on Security and Privacy (SP), San Francisco, CA, USA.","DOI":"10.1109\/SP.2019.00029"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Hitaj, B., Ateniese, G., and Perez-Cruz, F. (November, January 30). Deep models under the GAN: Information leakage from collaborative deep learning. Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, Dallas, TX, USA.","DOI":"10.1145\/3133956.3134012"},{"key":"ref_30","unstructured":"Price, K., Storn, R.M., and Lampinen, J.A. (2006). Differential Evolution: A Practical Approach to Global Optimization, Springer Science & Business Media."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Abadi, M., Chu, A., Goodfellow, I., McMahan, H.B., Mironov, I., Talwar, K., and Zhang, L. (2016, January 24\u201328). Deep Learning with Differential Privacy. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, New York, NY, USA. Association for Computing Machinery.","DOI":"10.1145\/2976749.2978318"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1016\/j.neunet.2019.10.001","article-title":"Privacy-enhanced multi-party deep learning","volume":"121","author":"Gong","year":"2020","journal-title":"Neural Netw."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/25\/11\/1551\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:24:17Z","timestamp":1760131457000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/25\/11\/1551"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,16]]},"references-count":32,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2023,11]]}},"alternative-id":["e25111551"],"URL":"https:\/\/doi.org\/10.3390\/e25111551","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,16]]}}}