A Comprehensive Survey on Security and Privacy for Electronic Health Data.

Recently, the integration of state-of-the-art technologies, such as modern sensors, networks, and cloud computing, has revolutionized the conventional healthcare system. However, security concerns have increasingly been emerging due to the integration of technologies. Therefore, the security and privacy issues associated with e-health data must be properly explored. In this paper, to investigate the security and privacy of e-health systems, we identified major components of the modern e-health systems (i.e., e-health data, medical devices, medical networks and edge/fog/cloud). Then, we reviewed recent security and privacy studies that focus on each component of the e-health systems. Based on the review, we obtained research taxonomy, security concerns, requirements, solutions, research trends, and open challenges for the components with strengths and weaknesses of the analyzed studies. In particular, edge and fog computing studies for e-health security and privacy were reviewed since the studies had mostly not been analyzed in other survey papers.

Self-Adaptive Framework Based on MAPE Loop for Internet of Things.

The Internet of Things (IoT) connects a wide range of objects and the types of environments in which IoT can be deployed dynamically change. Therefore, these environments can be modified dynamically at runtime considering the emergence of other requirements. Self-adaptive software alters its behavior to satisfy the requirements in a dynamic environment. In this context, the concept of self-adaptive software is suitable for some dynamic IoT environments (e.g., smart greenhouses, smart homes, and reality applications). In this study, we propose a self-adaptive framework for decision-making in an IoT environment at runtime. The framework comprises a finite-state machine model design and a game theoretic decision-making method for extracting efficient strategies. The framework was implemented as a prototype and experiments were conducted to evaluate its runtime performance. The results demonstrate that the proposed framework can be applied to IoT environments at runtime. In addition, a smart greenhouse-based use case is included to illustrate the usability of the proposed framework.

Imaging findings of invasive micropapillary carcinoma of the breast.

PURPOSE: The purpose of this study is to evaluate imaging and histopathologic findings including the immunohistochemical characteristics of invasive micropapillary carcinoma (IMPC) of the breast. METHODS: Twenty-nine patients diagnosed with IMPC were included in the present study. Mammographic, sonographic, and magnetic resonance imaging (MRI) findings were analyzed retrospectively according to the American College of Radiology Breast Imaging Reporting and Data System lexicon. (18)F-fluorodeoxyglucose positron emission tomography-computed tomography (PET-CT) findings were also evaluated. Microscopic slides of surgical specimens were reviewed in consensus by two pathologists with a specialty in breast pathology. RESULTS: Most IMPCs presented as a high density irregular mass with a non-circumscribed margin associated with microcalcifications on mammography, as an irregular hypoechoic mass with a spiculated margin on ultrasound, and as irregular spiculated masses with washout patterns on MRI. PET-CT showed a high maximum standardized uptake value (SUVmax) (mean, 11.2). Axillary nodal metastases were identified in 65.5% of the patients. Immunohistochemical studies showed high positivities for estrogen receptor and c-erbB-2 (93.1% and 51.7µ, respectively). CONCLUSION: Even though the imaging characteristics of IMPCs are not distinguishable from typical invasive ductal carcinomas, this tumor type frequently results in nodal metastases and high positivities for both estrogen receptor and c-erbB-2. The high SUVmax value that is apparent on PET-CT might be helpful in the diagnosis of IMPC.