Biblio

This research provides security and safety extensions to a blockchain based solution whose target is e-health. The Advanced Blockchain platform is extended with intelligent monitoring for security and machine learning for detecting patient treatment medication safety issues. For the reasons of stringent HIPAA, HITECH, EU-GDPR and other regional regulations dictating security, safety and privacy requirements, the e-Health blockchains have to cover mandatory disclosure of violations or enforcements of policies during transaction flows involving healthcare. Our service solution further provides the benefits of resolving the abnormal flows of a medical treatment process, providing accountability of the service providers, enabling a trust health information environment for institutions to handle medication safely, giving patients a better safety guarantee, and enabling the authorities to supervise the security and safety of e-Health blockchains. The capabilities can be generalized to support a uniform smart solution across industry in a variety of blockchain applications.

Policy design is an important part of software development. As security breaches increase in variety, designing a security policy that addresses all potential breaches becomes a nontrivial task. A complete security policy would specify rules to prevent breaches. Systematically determining which, if any, policy clause has been violated by a reported breach is a means for identifying gaps in a policy. Our research goal is to help analysts measure the gaps between security policies and reported breaches by developing a systematic process based on semantic reasoning. We propose SEMAVER, a framework for determining coverage of breaches by policies via comparison of individual policy clauses and breach descriptions. We represent a security policy as a set of norms. Norms (commitments, authorizations, and prohibitions) describe expected behaviors of users, and formalize who is accountable to whom and for what. A breach corresponds to a norm violation. We develop a semantic similarity metric for pairwise comparison between the norm that represents a policy clause and the norm that has been violated by a reported breach. We use the US Health Insurance Portability and Accountability Act (HIPAA) as a case study. Our investigation of a subset of the breaches reported by the US Department of Health and Human Services (HHS) reveals the gaps between HIPAA and reported breaches, leading to a coverage of 65%. Additionally, our classification of the 1,577 HHS breaches shows that 44% of the breaches are accidental misuses and 56% are malicious misuses. We find that HIPAA's gaps regarding accidental misuses are significantly larger than its gaps regarding malicious misuses.

A fundamental problem in the specification of regulatory privacy policies such as the Health Insurance Portability and Accountability Act (HIPAA) in a computer system is to state the policies precisely, consistent with their high-level intuition. In this paper, we propose UML sequence diagrams as a practical means to graphically express privacy policies. A graphical representation allows decision-makers such as application domain experts and security architects to easily verify and confirm the expected behavior. Once intuitively confirmed, our work in this article introduces an algorithmic approach to formalizing the semantics of sequence diagrams in terms of linear temporal logic (LTL) templates. In all the templates, different semantic aspects are expressed as separate, yet simple LTL formulas that can be composed to define the complex semantics of sequence diagrams. The formalization enables us to leverage the analytical powers of automated decision procedures for LTL formulas to determine if a collection of sequence diagrams is consistent, independent, etc. and also to verify if a system design conforms to the privacy policies. We evaluate our approach by modeling and analyzing a substantial subset of HIPAA rules using sequence diagrams.