A privacy‐preserving framework for smart context‐aware healthcare applications

Abstract

Internet of things (IoT) is a disruptive paradigm with wide ranging applications including healthcare, manufacturing, transportation and retail. Within healthcare, smart connected wearable devices are widely used to achieve improved wellbeing, quality of life and security of citizens. Such connected devices generate significant amount of data containing sensitive information about patient requiring adequate protection and privacy assurance. Unauthorized access to an individual's private data constitutes a breach of privacy leading to catastrophic outcomes for an individuals personal and professional life. Furthermore, breach of privacy may also lead to financial loss to the governing body such as those proposed as part of the General Data Protection Regulation (GDPR) in Europe. Furthermore, while mobility afforded by smart devices enables ease of monitoring, portability and pervasive processing, it also introduces challenges with respect to scalability, reliability and context-awareness for its applications. This paper is focused on privacy preservation within smart context-aware healthcare with a special emphasis on privacy assurance challenges within the Electronic Transfer of Prescription (ETP). To this extent, we present a case for a comprehensive, coherent, and dynamic privacypreserving system for smart healthcare to protect sensitive user data. Based on a thorough analysis of existing privacy preservation models we propose an enhancement for the widely used Salford model to achieve privacy preservation against masquerading and impersonation threats. The proposed model therefore improves privacy assurance for cutting edge IoT applications such as smart healthcare whilst addressing unique challenges with respect to context-aware mobility of such applications.

Digital Health in Physicians' and Pharmacists' Office: A Comparative Study of e-Prescription Systems' Architecture and Digital Security in Eight Countries

“…One type of risk is tampering with a patient's medication intake instructions, which could cause the patient's death. Therefore, many researchers emphasize the need for security and privacy policies and protocols to use IoT solutions in health care (Al-Nayadi and Abawajy, 2007 ; Azad et al, 2019 ; Ball et al, 2003 ; Park and Moon, 2016 ). One crucial challenge of e-Prescription systems is whether the system's overall architecture should be centralized or decentralized.…”

Trends, Technologies, and Key Challenges in Smart and Connected Healthcare

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(τ, m)‐slicedBucket privacy model for sequential anonymization for improving privacy and utility

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“…The accumulated data can be of various type such as credit card transactions, phone calls, web browsing, social media activities, or Electronic Health Records (EHR), etc. [1][2][3]. Such data may contain private information e.g.…”

“…Transition Partition separates the multiple QIgroups (i.e. i52[3] ≔ part(i51[3]) ∃s≥m )) that exists in a single bucket and keep them in an individual sliced bucket that have minimum QI distance between them. The buckets with respect to QI columns are sorted in rule 20.…”