Smart City Gnosys

Smart city article details

Title	Security And Privacy-Preserving For Machine Learning Models: Attacks, Countermeasures, And Future Directions
ID_Doc	47886
Authors	El-Husseini F.; Noura H.; Vernier F.
Year	2024
Published	Proceedings of the 8th Cyber Security in Networking Conference: AI for Cybersecurity, CSNet 2024
DOI	http://dx.doi.org/10.1109/CSNet64211.2024.10851722
Abstract	As machine learning (ML) develops more essential in fields like healthcare, banking, autonomous systems, and smart cities, it encounters growing security and privacy concerns that threaten the integrity and reliability of these systems. This work tackles these difficulties by examining several defensive techniques, classifying security risks as data, model, communication, and entity attacks, and suggesting precise counter-measures to protect ML models. Additionally, it underscores the significance of lightweight and efficient security solutions, especially in resource-limited settings such as IoT and edge computing, where conventional approaches may not be feasible. This work emphasizes the necessity for continuous research in cryptographic and non-cryptographic methods to improve the security and privacy of ML systems, ensuring their secure and efficient implementation in diverse industries. Moreover, it encompasses future research objectives, including emphasizing the development of privacy-preserving techniques, the ability to withstand adversarial attacks, and the creation of scalable solutions to address upcoming issues, such as those presented by quantum computing. © 2024 IEEE.
Author Keywords	Crypto-graphic Solutions; Edge Computing; Machine Learning (ML); ML Attacks; Non-Cryptographic Solutions

Similar Articles

Id	Similarity	Authors	Title	Published
5172	0.885	Rodríguez E.; Otero B.; Canal R.	A Survey Of Machine And Deep Learning Methods For Privacy Protection In The Internet Of Things	Sensors, 23, 3 (2023)
36064	0.881	Alfahaid A.; Alalwany E.; Almars A.M.; Alharbi F.; Atlam E.; Mahgoub I.	Machine Learning-Based Security Solutions For Iot Networks: A Comprehensive Survey	Sensors, 25, 11 (2025)
23897	0.875	Bouhaddi M.; Adi K.	Enhancing Privacy In Machine Learning: A Robust Approach For Preventing Attribute Inference Attacks	Proceedings of the International Conference on Security and Cryptography (2024)
4273	0.869	Qureshi A.; Qureshi M.A.; Haider H.A.; Khawaja R.	A Review On Machine Learning Techniques For Secure Iot Networks	Proceedings - 2020 23rd IEEE International Multi-Topic Conference, INMIC 2020 (2020)
47748	0.861	Ghaffari A.; Jelodari N.; pouralish S.; derakhshanfard N.; Arasteh B.	Securing Internet Of Things Using Machine And Deep Learning Methods: A Survey	Cluster Computing, 27, 7 (2024)
47930	0.859	Gomez L.; Yu T.; Duverger P.	Security For Distributed Machine Learning	Proceedings of the International Conference on Security and Cryptography, 1 (2023)
23097	0.859	Sanaullah; Attaullah H.; Jungeblut T.	Encryption Techniques For Privacy-Preserving Cnn Models: Performance And Practicality In Urban Ai Applications	Urban-AI 2024 - Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances in Urban-AI (2024)
9015	0.854	Souissi H.; Mahamat M.; Jaber G.; Lakhlef H.; Bouabdallah A.	Analyses Of Recent Advances On Machine Learning-Based Trust Management For Mobile Iot Applications	2022 30th International Conference on Software, Telecommunications and Computer Networks, SoftCOM 2022 (2022)
43116	0.853	Bhadauria S.; Kumar M.; Singh N.P.	Privacy Preserving And Performance Improvement In Edgecomputing Using Machine Learning	2022 IEEE Global Conference on Computing, Power and Communication Technologies, GlobConPT 2022 (2022)
6593	0.852	Rao G.S.; Yuvaraj S.A.; Kondapi N.R.; Kumari A.R.; Palepu N.R.; Bharathi C.R.; Arulananth T.S.; Ebinezer M.J.D.	Advancements In Machine Learning For Iot: Ai-Driven Optimization And Security	Journal of Information Systems Engineering and Management, 10, 17 (2025)