Formal verification and complexity analysis of confidentiality aware textual clinical documents framework

Smart health-care is the innovation that leads to enhanced diagnostic tools, improved patient treatment, and gadgets that ease the quality of life for majority of people. Textual clinical documents about an individual contain sensitive and semantically corelated terms. Most privacy-preserving approaches are not designed to prevent confidentiality threats. Although, recent approaches improved the utility of published output with generalized terms retrieved from several medical and general-purpose knowledge bases like SNOMED-CT and MASH. However, these models work on predefined sensitive terms using Wikipedia articles instead of authentic benchmarks. These Information Content-based methods are not capable to achieve the best balance between privacy and utility. The existing approaches guarantee syntactic privacy by sanitization but lack semantic privacy for textual clinical data. Therefore, it is imperative to design a confidentiality-aware framework to overcome these problems. Our proposed Confidentiality aware Textual Clinical Data Framework use preprocessed combinations of the terms instead of all combinations and perform automatic detection and sanitization of the sensitive and semantically correlated terms. The probabilistic sampling-based method guarantees the semantic privacy. We use high-level Petri nets to perform formal modeling of our proposed approach. Furthermore, we have also performed a detailed complexity analysis of the proposed framework.