# Multilevel Privacy Assurance Evaluation of Healthcare Metadata
## 3 Multilevel Privacy Assurance Evaluation: Results and Evaluation
### Twelve Noise-Adding Plugins and Their Privacy Utility Tradeoff
In this regard, the REA model helped us discover who, how, and what leads to an un-anonymized metadata-share amongst vertically and horizontally distributed Dutch caregivers. REA's Insurance Model (IM) uncovered the key economic agents, their prime economic interactions in the Dutch care metadata share landscape from the patient's perspective. The health insurer and patient are key (active) economic (party) agents who sign the contract of health insurance where caregivers are initially the passive (party) agents. The (party) agents become committed to the contract clauses for the increment (inflow) and decrement (outflow) of economic resources. As an increment, the patient receives the cash inflow from the insurer to the caregivers (who activate with respective contracts registration by the patient) on each medical checkup visit, the care services from caregivers and preservation of PII (via contracts). In decrement, the patient pays monthly payments to the insurer and gives his or her PII as an input to the insurer and caregivers' metadata. The decremented resources become valuable to the receiver. The receiver then enjoys the authority to evaluate the imbalance (if there is any) between the materialization of economic resources. The economic resources are provided care for the physical, informational, and decisional security and their prior settlements such as cash payments, contract clauses ensuring privacy). A significant result of our extended REA IM model is that the privacy concerns take the form of a Materialized Privacy Claim (MPC). MPC emanates when an economic agent (either the patient or any other potent authority) finds the imbalance in patients' exchanged resources. This modeling approach explains the who, how, and why of money claims for illegal information disclosures as MPC. Recent MPCs include 460,000 euro against the HAGA hospital, 440,000 euro against OLVG Amsterdam hospital [80,81,86], and 50,000 euros against Menzis insurance company by Dutch DPO. These incidents also suggest that data-utility-prone healthcare metadata handling is in practice in the dutch healthcare landscape for better healthcare outcomes. At the third-party data processing level, the empirical evaluation is performed using PM tools and techniques on real-world healthcare event logs. In Process Mining, three basic information pieces are crucial for revealing important process information concerning the event log (dataset). For that, the event should be primarily connected with a case (i.e., individual), an activity, and two-time stamps (initiating and ending timestamps). A sequence of events for one case/individual is called a trace. Identifying one trace is enough for an adversary to exploit the PII of a data subject (individual). This research work utilized noise addition for privacy preservation of the data subjects to the real world event logs and checked its influence upon the data utility of the event logs using ProM. The noise addition for privacy preservation to the level of maximum data utility is well acknowledged in the field of differential privacy. The process requires time, money, and effort in the long run as it requires the tools to be inherently differentially private. Thisresearch provides a short-term, end-user-friendly approach where existing PM tools and noise-adding techniques are used for noise addition. The experiments revealed some noise-adding plugins that provide good data utility as well. The performed PM experiments add noise by interchanging the sequence of traces, cases, or activities. The detailed description is provided in Section 3.4 and in Table 4. The resulting event logs are impossible for anyone to locate the right person for any activity. The solution is short-term but worth performing for PM end-users who are interested in doing privacy-preserving healthcare data analytics. For data utility, the conformance checking indicators are evaluated using noise-adding plugins in the ProM tool.