Abstract
The dissemination of Electronic Health Records (EHRs) can be extremely beneficial for multidimensional medical research perspectives leveraging patient diagnoses to reliable prescription, clinical trials to disease surveillance and immunization to disease prevention. However, privacy preservation on anonymous release–shared with medical researchers (or intended recipients)-demands a privacy model that must be able to meet three challenges: 1) it should be able to strike a balance between the privacy and utility of released dataset; 2) it should be able to preserve the individual-based privacy; 3) it should be able to thwart the adversary in the presence of arbitrary updates (i.e. with any consistent insert/update/delete sequence) and especially chainable-auxiliary information. The main objective of this work is to propose a privacy model that meets these three criteria. In this work, we propose τ-safety privacy model for sequential publication that is able to meet all above-mentioned challenges. τ (events list) refers to the type of operations (e.g., insert, update, delete) that can be performed on an individual's record in any release. The results of our experiments prove that the proposed scheme achieves better anonymization quality and query accuracy in comparison with m-invariance against τ attacks in external and internal updates.