Comprehensive Security and Privacy Framework against Malicious Insider in Cloud-based Machine Learning

سال انتشار: 1403
محل انتشار: مجله محاسبات و امنیت، دوره: 11، شماره: 1
کد COI اختصاصی: JR_JCSE-11-1_005
زبان مقاله: انگلیسی
تعداد مشاهده: 113

نویسندگان

University of Engineering and Technology, Lahore, Pakistan.

چکیده

Cloud-based machine learning has become an increasingly popular approach for training and deploying machine learning models, thanks to its scalability, cost-effectiveness, and ease of access. However, the use of cloud-based machine learning also introduces new security and privacy challenges, particularly with respect to insider threats. In this proposed research project, we aim to develop a multi-faceted approach to enhancing security and privacy in cloud-based machine learning. Our approach will draw on a range of techniques, including fully homomorphic encryption, multi-factor authentication. The proposed framework conducts a comprehensive evaluation using a variety of datasets and use cases, and this approach provides higher security and privacy as compared to existing security and privacy frameworks for cloud-based machine learning. The ultimate goal is to provide practical and effective solutions for enhancing security and privacy in cloud-based machine learning, and to contribute to the ongoing efforts to address the challenges of insider threats in this rapidly evolving field.

کلیدواژه ها

Security, Privacy, Malicious Insider, Cloud-based Machine Learning

اطلاعات بیشتر در مورد COI

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