An analytical solution for consent management in patient privacy preservation

Abstract

With the growing awareness and enforcement of patient rights, patients are empowered with increasing control on their medical information. In many situations, laws and regulation rules require the acquisition of patients' consent before one can access the patients' health data. However, in practice, patients oftentimes have difficulties determining whether they should permit or deny a certain access request. In this article, we propose an analytical approach to assist patients in the consent management of their medical information. Our consent management system employs a statistical learning method that evaluates the benefits and risks associated with access requests, so as to make personalized recommendation on consent decisions. Multiple factors are considered in the assessment process, including the importance of the request, the sensitivity of the requested information, and correlation information. We have implemented a prototype of our solution and performed evaluation with large-scale medical records.

Privacy Preserving Risk Mitigation Approach for Healthcare Domain

“…As proposed in [29], the relevance of the meant information has been calculated and stored in the DRM. Nonetheless, one effective method of calculating relevance information between the diseases is to pedical data is found by analyzing the correlation incurred of the different records.…”

“…If the difference is large, the risk is high and vice versa. The authors utilized the widely accepted definition of risk assessment as defined by NIST [29] of which calculates risk as the likelihood of an event multiplied by the possible impact. Similarly, [19] have utilized fuzzy inference techniques to calculate risk values for enforcing access control.…”

“…Risk mitigation [29] approaches can be employed to lower the risk indicated by an access request. Several works have proposed different approaches to mitigate the risk.…”

“…is computed, and since the objective is to find the maximum number of data objects that can be revealed, with accordance to HIPPA rule of privacy, lines (26)(27)(28)(29)(30)(31)(32)(33) show the set of data that can be exposed.…”

“…According to the discussion in part A of Section 4, the computed Risk Measure of a combination exceeds the For instances with the same Max w i and length, the first generated has the highest sensitivity weights while the last has the lowest ones. (27)(28)(29)(30)(31) in Figure 5 show the approach by utilizing the while loop. To illustrate, consider the previous example in Table 9 where 5 n = and 4 r = .…”