Biblio

The risk posed by high-profile data breaches has raised the stakes for adhering to data access policies for many organizations, but the complexity of both the policies themselves and the applications that must obey them raises significant challenges. To mitigate this risk, fine-grained audit of access to private data has become common practice, but this is a costly, time-consuming, and error-prone process.We propose an approach for automating much of the work required for fine-grained audit of private data access. Starting from the assumption that the auditor does not have an explicit, formal description of the correct policy, but is able to decide whether a given policy fragment is partially correct, our approach gradually infers a policy from audit log entries. When the auditor determines that a proposed policy fragment is appropriate, it is added to the system's mechanized policy, and future log entries to which the fragment applies can be dealt with automatically. We prove that for a general class of attribute-based data policies, this inference process satisfies a monotonicity property which implies that eventually, the mechanized policy will comprise the full set of access rules, and no further manual audit is necessary. Finally, we evaluate this approach using a case study involving synthetic electronic medical records and the HIPAA rule, and show that the inferred mechanized policy quickly converges to the full, stable rule, significantly reducing the amount of effort needed to ensure compliance in a practical setting.

Medical organizations find it challenging to adopt cloud-based Electronic Health Records (EHR) services due to the risk of data breaches and the resulting compromise of patient data. Existing authorization models follow a patient-centric approach for EHR management, where the responsibility of authorizing data access is handled at the patients’ end. This creates significant overhead for the patient, who must authorize every access of their health record. It is also not practical given that multiple personnel are typically involved in providing care and that the patient may not always be in a state to provide this authorization.

Healthcare is traditionally a data-intensive domain, where physicians needs complete and updated anamnesis of their patients to take the best medical decisions. Dematerialization of the medical documents and the consequent health information systems to share electronic health records among healthcare providers are paving the way to an effective solution to this issue. However, they are also paving the way of non-negligible privacy issues that are limiting the full application of these technologies. Encryption is a valuable means to resolve such issues, however the current schemes are not able to cope with all the needs and challenges that the cloud-based sharing of electronic health records imposes. In this work we have investigated the use of a novel scheme where encryption is combined with biometric authentication, and defines a preliminary solution.

With the exponential growth in the usage of electronic medical records (EMR), the amount of data generated by the healthcare industry has too increased exponentially. These large amounts of data, known as “Big Data” is mostly unstructured. Special big data analytics methods are required to process the information and retrieve information which is meaningful. As patient information in hospitals and other healthcare facilities become increasingly electronic, Big Data technologies are needed now more than ever to manage and understand this data. In addition, this information tends to be quite sensitive and needs a highly secure environment. However, current security algorithms are hard to be implemented because it would take a huge amount of time and resources. Security protocols in Big data are also not adequate in protecting sensitive information in the healthcare. As a result, the healthcare data is both heterogeneous and insecure. As a solution we propose the Secure Pattern-Based Data Sensitivity Framework (PBDSF), that uses machine learning mechanisms to identify the common set of attributes of patient data, data frequency, various patterns of codes used to identify specific conditions to secure sensitive information. The framework uses Hadoop and is built on Hadoop Distributed File System (HDFS) as a basis for our clusters of machines to process Big Data, and perform tasks such as identifying sensitive information in a huge amount of data and encrypting data that are identified to be sensitive.