# Multilevel Privacy Assurance Evaluation of Healthcare Metadata
## 1 Introduction
### State of the Art
In this wake, bigger data and better algorithms have gradually mitigated the significance of privacy breaches of individuals in favor of their informed consent [2]. IC intake takes place at the onset of patients' PII collection. However, not only at the onset of data collection, rather each step in the data pipeline causes ethical issues, i.e., inaccuracy, inequality, non-transparency, and intrusion [21]. If emerging technologies are to be classified concerning their ethical issues, each one would have the prime issue of privacy protection [32]. Privacy-by-design parameters aim to technically integrate the privacy indicators into the organization's Information Systems [14]. Privacy by design is system based and incorporates ISMS standard parameters in alignment with the organization's objectives and goals. Usually, PbD takes place in three steps: namely, minimization, enforcement, and transparency [15]. On the other hand, privacy by policy for organizations and individuals is the application of regulations, laws, policies, and processes by which personal information is managed [16]. Privacy by policy is mainly related to privacy-securing data processing of data subjects. Its byproduct is the Information Security Management System (ISMS), with availability, integrity, confidentiality, and accountability as its standard indicators. The first two indicators encompass \"patients security\", and the remaining two involve \"patients privacy\", which collectively come under the tag of Information Security [17]. Privacy Utility Tradeoff, PUT: To ensure the responsible healthcare metadata handling, the main concern, amongst others, is the Privacy Utility Tradeoff (PUT) [3,33]. PUT appears when the privacy-preserving measures (tools and techniques) start hampering the data utility after a point. Data utility is the precision of the analytical data results [34]. In healthcare, the PUT raises graver repercussions because of the involvement of highly sensitive Personally Identifiable Information (PII) on the one hand and the efficiency and effectiveness of healthcare services on the other [35]. Therefore, research studies focus on multiple domains separately. For example, privacy and IoT [36; 37; 38], privacy in AI, and machine learning [39; 40] privacy in Process Mining [41; 42; 43; 44]. This targeted attention lagged in paying concerted attention to the overall context of the issue. Privacy and Third-Party Data Processing using Process Mining: The privacy concerns ameliorate when healthcare metadata are shared with a third party (data processor) for further data evaluations using Process Mining (PM) tools and techniques. Process Mining (PM) is an emerging discipline that bridges the gap between data mining and business process management. PM involves process optimization and includes business deviations and bottlenecks finding by utilizing its discovery, enhancement, and conformance checking techniques [45; 46; 47; 48]. In PM, the event logs are extracted from an organization's Information System (IS) for an insightful evaluation of actual business proceedings [41; 44]. PM is not inherently privacy preserving and seeks external privacy preserving measures. Usefulness of Process Mining during the COVID-19 pandemic: PM as an online third-party metadata processing (collection of) technique(s) proved to be highly useful during the COVID-19 pandemic across domains. For example, by evaluating the metadata of Covid 19 patients in Unikliniek Aachen hospital, PM could facilitate the hospital in reconstructing their ICU model for cost and time-efficient treatments [49]. In the logistics domain, PM, using virtual desktop infrastructure, ensured productive remote controller operations while keeping intact the integrity and confidentiality of relevant business information [50]. PM is also highly applicable in automating the business processes in the post-COVID economy with the help of artificial intelligence and robotic process automation [51].