A novel approach of privacy-preserving data sharing system through data-tagging with role-based access control

Abstract

Purpose This paper aims to design a secure and seamless system that ensures quick sharing of health-care data to improve the privacy of sensitive health-care data, the efficiency of health-care infrastructure, effective treatment given to patients and encourage the development of new health-care technologies by researchers. These objectives are achieved through the proposed system, a “privacy-aware data tagging system using role-based access control for health-care data.” Design/methodology/approach Health-care data must be stored and shared in such a manner that the privacy of the patient is maintained. The method proposed, uses data tags to classify health-care data into various color codes which signify the sensitivity of data. It makes use of the ARX tool to anonymize raw health-care data and uses role-based access control as a means of ensuring only authenticated persons can access the data. Findings The system integrates the tagging and anonymizing of health-care data coupled with robust access control policies into one architecture. The paper discusses the proposed architecture, describes the algorithm used to tag health-care data, analyzes the metrics of the anonymized data against various attacks and devises a mathematical model for role-based access control. Originality/value The paper integrates three disparate topics – data tagging, anonymization and role-based access policies into one seamless architecture. Codifying health-care data into different tags based on International Classification of Diseases 10th Revision (ICD-10) codes and applying varying levels of anonymization for each data tag along with role-based access policies is unique to the system and also ensures the usability of data for research.

University students' digital spaces in online learning communities: Implications for understanding, protecting and maintaining privacy

“…Anonymity can also be preferred when sharing good and effective examples with other students (Basso et al, 2016;Biehl et al, 2013;Chang, 2021). Since not revealing the real identity for student privacy supports security in OLC, it has been suggested in the current study and different studies in the literature (Anwar, 2021;Basso et al, 2016;Biehl et al, 2013;Chang, 2021;Garg et al, 2023;Zhang, Luo, et al, 2023;Zhang, Pan, et al, 2023).…”

“…In this study, it was obtained from Delphi interviews that not revealing the real identity and using contextual identity can prevent privacy violations. This measure has also been suggested in different studies (Anwar, 2021;Garg et al, 2023;Zhang, Pan, et al, 2023). The possibility of participating in the activities with an anonymous identity keeps the student in a safe environment (Zhang, Luo, et al, 2023).…”

An adaptive data-driven architecture for mental health care applications

“…Two key measures, role-based access control (RBAC) and multi-factor authentication (MFA), play pivotal roles in safeguarding sensitive data. RBAC enables mental health care organizations to assign specific access privileges to users based on their roles and responsibilities ( Garg et al, 2023 ), preventing unauthorized access and ensuring users only access necessary information for their job functions. MFA enhances security by requiring users to provide multiple forms of authentication, such as using hash functions ( Midha et al, 2023 ).…”