TY - BOOK ED - Neuhaus, Christian ED - Polze, Andreas T1 - Cloud security mechanisms N2 - Cloud computing has brought great benefits in cost and flexibility for provisioning services. The greatest challenge of cloud computing remains however the question of security. The current standard tools in access control mechanisms and cryptography can only partly solve the security challenges of cloud infrastructures. In the recent years of research in security and cryptography, novel mechanisms, protocols and algorithms have emerged that offer new ways to create secure services atop cloud infrastructures. This report provides introductions to a selection of security mechanisms that were part of the "Cloud Security Mechanisms" seminar in summer term 2013 at HPI. N2 - Cloud Computing hat deutliche Kostenersparnisse und verbesserte Flexibilität bei der Bereitstellung von Computer-Diensten ermöglicht. Allerdings bleiben Sicherheitsbedenken die größte Herausforderung bei der Nutzung von Cloud-Diensten. Die etablierten Mechanismen für Zugriffskontrolle und Verschlüsselungstechnik können die Herausforderungen und Probleme der Sicherheit von Cloud-Infrastrukturen nur teilweise lösen. In den letzten Jahren hat die Forschung jedoch neue Mechanismen, Protokolle und Algorithmen hervorgebracht, welche neue Möglichkeiten eröffnen die Sicherheit von Cloud-Anwendungen zu erhöhen. Dieser technische Bericht bietet Einführungen zu einigen dieser Mechanismen, welche im Seminar "Cloud Security Mechanisms" im Sommersemester 2013 am HPI behandelt wurden. T3 - Technische Berichte des Hasso-Plattner-Instituts für Digital Engineering an der Universität Potsdam - 87 KW - Cloud KW - Sicherheit KW - Privacy KW - Datenvertraulichkeit KW - Threshold Cryptography KW - Bitcoin KW - Homomorphe Verschlüsselung KW - Differential Privacy KW - cloud KW - security KW - privacy KW - confidentiality KW - threshold cryptography KW - bitcoin KW - homomorphic encryption KW - differential privacy Y1 - 2014 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-68168 SN - 978-3-86956-281-0 SN - 1613-5652 SN - 2191-1665 IS - 87 PB - Universitätsverlag Potsdam CY - Potsdam ER - TY - JOUR A1 - Ermakova, Tatiana A1 - Fabian, Benjamin A1 - Zarnekow, Ruediger T1 - Improving Individual Acceptance of Health Clouds through Confidentiality Assurance JF - Applied clinical informatics N2 - Background: Cloud computing promises to essentially improve healthcare delivery performance. However, shifting sensitive medical records to third-party cloud providers could create an adoption hurdle because of security and privacy concerns. Methods: We empirically investigate our research question by a survey with over 260 full responses. For the setting with a high confidentiality assurance, we base on a recent multi-cloud architecture which provides very high confidentiality assurance through a secret-sharing mechanism: Health information is cryptographically encoded and distributed in a way that no single and no small group of cloud providers is able to decode it. KW - Cloud computing KW - cloud service KW - cloud storage KW - data security KW - privacy KW - confidentiality KW - acceptance process Y1 - 2016 U6 - https://doi.org/10.4338/ACI-2016-07-RA-0107 SN - 1869-0327 VL - 7 SP - 983 EP - 993 PB - Schattauer CY - Stuttgart ER - TY - JOUR A1 - Adnan, Hassan Sami A1 - Srsic, Amanda A1 - Venticich, Pete Milos A1 - Townend, David M.R. T1 - Using AI for mental health analysis and prediction in school surveys JF - European journal of public health N2 - Background: Childhood and adolescence are critical stages of life for mental health and well-being. Schools are a key setting for mental health promotion and illness prevention. One in five children and adolescents have a mental disorder, about half of mental disorders beginning before the age of 14. Beneficial and explainable artificial intelligence can replace current paper- based and online approaches to school mental health surveys. This can enhance data acquisition, interoperability, data driven analysis, trust and compliance. This paper presents a model for using chatbots for non-obtrusive data collection and supervised machine learning models for data analysis; and discusses ethical considerations pertaining to the use of these models. Methods: For data acquisition, the proposed model uses chatbots which interact with students. The conversation log acts as the source of raw data for the machine learning. Pre-processing of the data is automated by filtering for keywords and phrases. Existing survey results, obtained through current paper-based data collection methods, are evaluated by domain experts (health professionals). These can be used to create a test dataset to validate the machine learning models. Supervised learning can then be deployed to classify specific behaviour and mental health patterns. Results: We present a model that can be used to improve upon current paper-based data collection and manual data analysis methods. An open-source GitHub repository contains necessary tools and components of this model. Privacy is respected through rigorous observance of confidentiality and data protection requirements. Critical reflection on these ethics and law aspects is included in the project. Conclusions: This model strengthens mental health surveillance in schools. The same tools and components could be applied to other public health data. Future extensions of this model could also incorporate unsupervised learning to find clusters and patterns of unknown effects. KW - ethics KW - artificial intelligence KW - adolescent KW - child KW - confidentiality KW - health personnel KW - mental disorders KW - mental health KW - personal satisfaction KW - privacy KW - school (environment) KW - statutes and laws KW - public health medicine KW - surveillance KW - medical KW - prevention KW - datasets KW - machine learning KW - supervised machine learning KW - data analysis Y1 - 2020 U6 - https://doi.org/10.1093/eurpub/ckaa165.336 SN - 1101-1262 SN - 1464-360X VL - 30 SP - V125 EP - V125 PB - Oxford Univ. Press CY - Oxford [u.a.] ER -