Titlebook: Availability, Reliability, and Security in Information Systems; IFIP WG 8.4, 8.9, TC Francesco Buccafurri,Andreas Holzinger,Edgar Weipp Con

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Politikwissenschaftliche Paperbacksand indirectly manipulating and interacting with an algorithm. For this purpose, we selected the Ant Colony Optimization (ACO) framework, and use it on the Traveling Salesman Problem (TSP) which is of high importance in solving many practical problems in health informatics, e.g. in the study of prot

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https://doi.org/10.1007/978-3-663-08049-7onstruction of AE should satisfies the properties of efficiency and upper security bound due to limitation of power and memory for the constrained device. Therefore, we propose a blockcipher based AE that satisfies upper privacy security bound . and it operates in parallel mode. It doesn’t need decr

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Problemstellung und Gang der Untersuchung,ound rather than efficiency. Therefore, a more efficient construction of blockcipher based compression function is proposed, where it provides higher efficiency-rate including a satisfactory collision security bound. The efficiency-rate . of the proposed scheme is .. Furthermore, the collision secur

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Theorie der Unternehmenskooperation,onal periodicity levels into consideration. Each model captures transitions between times and states according to shorter parts of the sequence, which is eventually reconstructed. Anonymity is provided by utilizing only elements of the model that guarantee differential privacy. Furthermore, we devel

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Theorie der Unternehmenskooperation,lements the primitive instructions foreseen by the architecture. An open-source reference implementation of this architecture is provided for the R language. We use these results to argue that the tension between medical research and privacy requirements can be technically alleviated and we outline

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https://doi.org/10.1007/978-3-7091-0402-6ditions. Their choices include a re-design of their data infrastructure as well as preventive actions like anonymization of databases per default. Therefore, insights into the effects of perturbed/anonymized knowledge bases on the quality of machine learning results are a crucial basis for successfu