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Title: Privacy Preserving Data Publishing Using Access Control in Hybrid Cloud
Authors: Kanwal, Tehsin
Keywords: Physical Sciences
Computer & IT
Issue Date: 2022
Publisher: COMSATS University, Islamabad
Abstract: Privacy Preservation is a dynamic, challenging and multifaceted concept. Privacy refers to the resolution of issues concerning an individual’s personal information and the disclosure of that personal information. In recent years, Electronic Health Records (EHRs) publishing becomes highly vulnerable with the emergence of cloud computing. Besides the benefits, the privacy and security of publicly released EHRs is a critical concern. To address this concern, an extensive study and analysis of privacy-preserving models, techniques, and their applications for protection of cloud based EHRs is performed in this work. We have conducted a thorough investigation and analysis of privacy-preserving models and techniques for publishing and collection of EHRs data. Meanwhile, the importance of maintaining a balance between privacy and the utility of EHRs data is also emphasized. The proposed research introduce the use of hybrid approach for fine grained access control mechanisms and privacy-preserving anonymity-based techniques. eXtensible Access Control Mark-up Language (XACML) based authorization models are provided in hybrid cloud. Data outsourcing scenarios for EHRs data having multiple sensitive attributes and multiple occurrences of patient data are carefully investigated. The evaluation of privacy preservation mechanisms for adversarial disclosures using multiple datasets with multiple sensitive attributes scenarios is a challenging and complicated task. The proposed privacy models are highly effective against possible privacy disclosures and unauthorized EHRs’ access. In this work, we have also proposed secure and efficient EHRs data collection mechanisms. Formal modelling, analysis and verification of proposed access control and privacy preserving models in terms of correctness and privacy disclosures invalidation is also performed in this research work.
Gov't Doc #: 27527
Appears in Collections:PhD Thesis of All Public / Private Sector Universities / DAIs.

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