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Abstract:While the opening of data has become a common practice for both governments and companies, many datasets are still not published since they might violate privacy regulations. The risk on privacy violations is a factor that often blocks the publication of data and results in a reserved attitude of governments and companies. Additionally, even published data, which might seem privacy compliant, can violate user privacy due to the leakage of real user identities. This paper proposes a privacy risk scoring model for open data architectures to analyse and reduce the risks associated with the opening of data. The key elements consist of a new set of open data attributes reflecting privacy risks versus benefits trades-offs. Further, these attributes are evaluated using a decision engine and a scoring matrix intro a privacy risk indicator (PRI) and a privacy risk mitigation measure (PRMM). Privacy Risk Indicator (PRI) represents the predicted value of privacy risks associated with opening such data and privacy risk mitigation measures represent the measurements need to be applied on the data to avoid the expected privacy risks. The model is exemplified through five real use cases concerning open datasets.

Alzheimer’s disease (AD) is a form of brain disorder that causes functions’ loss in a person’s daily activity. Due to the tremendous progress of Alzheimer’s patients and the lack of accurate diagnostic tools, early detection and classification of Alzheimer’s disease are open research areas. Accurate detection of Alzheimer’s disease in an effective way is one of the many researchers’ goals to limit or overcome the disease progression. The main objective of the current survey is to introduce a comprehensive evaluation and analysis of the most recent studies for AD early detection and classification under the state-of-the-art deep learning approach. The article provides a simplified explanation of the system stages such as imaging, preprocessing, learning, and classification. It addresses broad categories of structural, functional, and molecular imaging in AD. The included modalities are magnetic resonance imaging (MRI; both structural and functional) and positron emission tomography (PET; for assessment of both cerebral metabolism and amyloid). It reviews the process of pre-processing techniques to enhance the quality. Additionally, the most common deep learning techniques used in the classification process will be discussed. Although deep learning with preprocessing images has achieved high performance as compared to other techniques, there are some challenges. Moreover, it will also review some challenges in the classification and preprocessing image process over some articles what they introduce, and techniques used, and how they solved these problems.

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