Healthcare data in cloud computing facilitates the treatment of patients efficiently by sharing information about personal health data between the healthcare providers for medical consultation. Furthermore, retaining the confidentiality of data and patients' identity is a another challenging task. This paper presents the concept of an access control-based (AC) privacy preservation model for the mutual authentication of users and data owners in the proposed digital system. The proposed model offers a high-security guarantee and high efficiency. The proposed digital system consists of four different entities, user, data owner, cloud server, and key generation center (KGC). This approach makes the system more robust and highly secure, which has been verified with multiple scenarios. Besides, the proposed model consisted of the setup phase, key generation phase, encryption phase, validation phase, access control phase, and data sharing phase. The setup phases are run by the data owner, which takes input as a security parameter and generates the system master key and security parameter. Then, in the key generation phase, the private key is generated by KGC and is stored in the cloud server. After that, the generated private key is encrypted. Then, the session key is generated by KGC and granted to the user and cloud server for storing, and then, the results are verified in the validation phase using validation messages. Finally, the data is shared with the user and decrypted at the user-end. The proposed model outperforms other methods with a maximal genuine data rate of 0.91.
Hierarchical Dual-Strategy Unlearning for Biomedical and Healthcare Intelligence Using Imperfect and Privacy-Sensitive Medical Data
Department of Neurosurgery, Qilu Hospital of Shandong University and Institute of Brain and Brain-Inspired Science（齐鲁医院山东大学神经外科和脑科学与脑启发科学研究院）；Department of Neurosurgery, The Affiliated Hospital of Qingdao University（青岛大学附属医院神经外科）；Department of Neurosurgery, Peking Union Medical College Hospital（北京地坛医院神经外科）；School of Software and Microelectronics, Peking University（北京大学软件与微电子学院）；College of Artificial Intelligence, Dalian Maritime University（大连海事大学人工智能学院）；University of Colorado Boulder（科罗拉多大学波尔德分校）；Zhengzhou University（郑州大学）；The University of Hong Kong（香港大学）；Georgia Institute of Technology（佐治亚理工学院）
Privacy Challenges and Solutions in Retrieval-Augmented Generation-Enhanced LLMs for Healthcare Chatbots: A Review of Applications, Risks, and Future Directions
Centre for Smart Health, School of Nursing（智能健康中心、护理学院）；The Hong Kong Polytechnic University（香港理工大学）；Department of Electrical and Electronic Engineering（电子与电气工程系）；Faculty of Health Sciences（健康科学学院）；University of Maribor（马里博大学）
Forget-MI: Machine Unlearning for Forgetting Multimodal Information in Healthcare Settings
Department of Machine Learning, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE Department of Computer Vision, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE Department of Computer Science, Michigan State University, United States 0.2cm
Differentially private federated learning with Byzantine-robust aggregation: A cross-domain framework for secure model training in banking and healthcare systems
When Machine Learning Meets Privacy: A Survey and Outlook
University of Technology Sydney（悉尼科技大学）；Data61, CSIRO（澳大利亚联邦科学与工业研究组织 Data61）；The University of Sydney（悉尼大学）；La Trobe University（乐卓博大学）；The University of Melbourne（墨尔本大学）
Towards Blockchain-Assisted Privacy-Aware Data Sharing For Edge Intelligence: A Smart Healthcare Perspective
Data61, CSIRO（联邦科学与工业研究组织数据61研究所）；Victoria University（维多利亚大学）；University of Electronic Science and Technology of China（电子科技大学）；University of Technology Sydney（悉尼科技大学）；CNPIEC KEXIN LTD（中国图书进出口科信有限公司）；Xidian University（西安电子科技大学）
Tighter Generalization Bounds for Iterative Differentially Private Learning Algorithms
UBTECH Sydney AI Centre, School of Computer Science, Faculty of Engineering, the University of Sydney（悉尼大学工程学院计算机科学学院优必选悉尼人工智能中心）；University of Science and Technology of China（中国科学技术大学）
Patchwork Learning: A Paradigm Towards Integrative Analysis across Diverse Biomedical Data Sources
Cornell University（康奈尔大学）；Weill Cornell Medicine（威尔康奈尔医学院）；Cornell Tech（康奈尔科技学院）；Weill Cornell Medical School（威尔康奈尔医学院）；University of Pennsylvania（宾夕法尼亚大学）；Michigan State University（密歇根州立大学）
