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Privacy and its protection is an important part of the culture in the USA and Europe. Literature in this field lacks empirical data from Japan. Thus, it is difficult– especially for foreign researchers – to understand the situation in Japan. To get a deeper understanding we examined the perception of a topic that is closely related to privacy: the perceived benefits of sharing data and the willingness to share in respect to the benefits for oneself, others and companies. We found a significant impact of the gender to each of the six analysed constructs.
This paper provides an assessment framework for privacy policies of Internet of Things Services which is based on particular GDPR requirements. The objective of the framework is to serve as supportive tool for users to take privacy-related informed decisions. For example when buying a new fitness tracker, users could compare different models in respect to privacy friendliness or more particular aspects of the framework such as if data is given to a third party. The framework consists of 16 parameters with one to four yes-or-no-questions each and allows the users to bring in their own weights for the different parameters. We assessed 110 devices which had 94 different policies. Furthermore, we did a legal assessment for the parameters to deal with the case that there is no statement at all regarding a certain parameter. The results of this comparative study show that most of the examined privacy policies of IoT devices/services are insufficient to address particular GDPR requirements and beyond. We also found a correlation between the length of the policy and the privacy transparency score, respectively.
In the upcoming years, the internet of things (IoT)will enrich daily life. The combination of artificial intelligence(AI) and highly interoperable systems will bring context-sensitive multi-domain services to reality. This paper describesa concept for an AI-based smart living platform with open-HAB, a smart home middleware, and Web of Things (WoT) askey components of our approach. The platform concept con-siders different stakeholders, i.e. the housing industry, serviceproviders, and tenants. These activities are part of the Fore-Sight project, an AI-driven, context-sensitive smart living plat-form.
Optimal investment decisions by institutional investors require accurate predictions with respect to the development of stock markets. Motivated by previous research that revealed the unsatisfactory performance of existing stock market prediction models, this study proposes a novel prediction approach. Our proposed system combines Artificial Intelligence (AI) with data from Virtual Investment Communities (VICs) and leverages VICs’ ability to support the process of predicting stock markets. An empirical study with two different models using real data shows the potential of the AI-based system with VICs information as an instrument for stock market predictions. VICs can be a valuable addition but our results indicate that this type of data is only helpful in certain market phases.
This article discusses the counterpart of interactive machine learning, i.e., human learning while being in the loop in a human-machine collaboration. For such cases we propose the use of a Contradiction Matrix to assess the overlap and the contradictions of human and machine predictions. We show in a small-scaled user study with experts in the area of pneumology (1) that machine-learning based systems can classify X-rays with respect to diseases with a meaningful accuracy, (2) humans partly use contradictions to reconsider their initial diagnosis, and (3) that this leads to a higher overlap between human and machine diagnoses at the end of the collaboration situation. We argue that disclosure of information on diagnosis uncertainty can be beneficial to make the human expert reconsider her or his initial assessment which may ultimately result in a deliberate agreement. In the light of the observations from our project, it becomes apparent that collaborative learning in such a human-in-the-loop scenario could lead to mutual benefits for both human learning and interactive machine learning. Bearing the differences in reasoning and learning processes of humans and intelligent systems in mind, we argue that interdisciplinary research teams have the best chances at tackling this undertaking and generating valuable insights.
With the rapid growth of technology in recent years, we are surrounded by or even dependent on the use of technological devices such as smartphones as they are now an indispensable part of our life. Smartphone applications (apps) provide a wide range of utilities such as navigation, entertainment, fitness, etc. To provide such context-sensitive services to users, apps need to access users' data including sensitive ones, which in turn, can potentially lead to privacy invasions. To protect users against potential privacy invasions in such a vulnerable ecosystem, legislation such as the European Union General Data Protection Regulation (EU GDPR) demands best privacy practices. Therefore, app developers are required to make their apps compatible with legal privacy principles enforced by law. However, this is not an easy task for app developers to comprehend purely legal principles to understand what needs to be implemented. Similarly, bridging the gap between legal principles and technical implementations to understand how legal principles need to be implemented is another barrier to develop privacy-friendly apps. To this end, this paper proposes a privacy and security design guide catalog for app developers to assist them in understanding and adopting the most relevant privacy and security principles in the context of smartphone apps. The presented catalog is aimed at mapping the identified legal principles to practical privacy and security solutions that can be implemented by developers to ensure enhanced privacy aligned with existing legislation. Through conducting a case study, it is confirmed that there is a significant gap between what developers are doing in reality and what they promise to do. This paper provides researchers and developers of privacy-related technicalities an overview of the characteristics of existing privacy requirements needed to be implemented in smartphone ecosystems, on which they can base their work.
Security has become one of the primary factors that cloud customers consider when they select a cloud provider for migrating their data and applications into the Cloud. To this end, the Cloud Security Alliance (CSA) has provided the Consensus Assessment Questionnaire (CAIQ), which consists of a set of questions that providers should answer to document which security controls their cloud offerings support. In this paper, we adopted an empirical approach to investigate whether the CAIQ facilitates the comparison and ranking of the security offered by competitive cloud providers. We conducted an empirical study to investigate if comparing and ranking the security posture of a cloud provider based on CAIQ’s answers is feasible in practice. Since the study revealed that manually comparing and ranking cloud providers based on the CAIQ is too time-consuming, we designed an approach that semi-automates the selection of cloud providers based on CAIQ. The approach uses the providers’ answers to the CAIQ to assign a value to the different security capabilities of cloud providers. Tenants have to prioritize their security requirements. With that input, our approach uses an Analytical Hierarchy Process (AHP) to rank the providers’ security based on their capabilities and the tenants’ requirements. Our implementation shows that this approach is computationally feasible and once the providers’ answers to the CAIQ are assessed, they can be used for multiple CSP selections. To the best of our knowledge this is the first approach for cloud provider selection that provides a way to assess the security posture of a cloud provider in practice.
Augmented reality (AR) gained much public attention since the success of Pok´emon Go in 2016. Technology companies like Apple or Google are currently focusing primarily on mobile AR (MAR) technologies, i.e. applications on mobile devices, like smartphones or tablets. Associated privacy issues have to be investigated early to foster market adoption. This is especially relevant since past research found several threats associated with the use of smartphone applications. Thus, we investigate two of the main privacy risks for MAR application users based on a sample of 19 of the most downloaded MAR applications for Android. First, we assess threats arising from bad privacy policies based on a machine-learning approach. Second, we investigate which smartphone data resources are accessed by the MAR applications. Third, we combine both approaches to evaluate whether privacy policies cover certain data accesses or not. We provide theoretical and practical implications and recommendations based on our results.
Advanced machine learning has achieved extraordinary success in recent years. “Active” operational risk beyond ex post analysis of measured-data machine learning could provide help beyond the regime of traditional statistical analysis when it comes to the “known unknown” or even the “unknown unknown.” While machine learning has been tested successfully in the regime of the “known,” heuristics typically provide better results for an active operational risk management (in the sense of forecasting). However, precursors in existing data can open a chance for machine learning to provide early warnings even for the regime of the “unknown unknown.”
Online Privacy: Towards Informational Self-Determination on the Internet (Dagstuhl Perspectives Workshop 11061) : Simone Fischer-Hübner, Chris Hoofnagle, Kai Rannenberg, Michael Waidner, Ioannis Krontiris and Michael Marhöfer Self-Repairing Programs (Dagstuhl Seminar 11062) : Mauro Pezzé, Martin C. Rinard, Westley Weimer and Andreas Zeller Theory and Applications of Graph Searching Problems (Dagstuhl Seminar 11071) : Fedor V. Fomin, Pierre Fraigniaud, Stephan Kreutzer and Dimitrios M. Thilikos Combinatorial and Algorithmic Aspects of Sequence Processing (Dagstuhl Seminar 11081) : Maxime Crochemore, Lila Kari, Mehryar Mohri and Dirk Nowotka Packing and Scheduling Algorithms for Information and Communication Services (Dagstuhl Seminar 11091) Klaus Jansen, Claire Mathieu, Hadas Shachnai and Neal E. Young
The Dagstuhl Perspectives Workshop "Online Privacy: Towards Informational Self-Determination on the Internet" (11061) has been held in February 6-11, 2011 at Schloss Dagstuhl. 30 participants from academia, public sector, and industry have identified the current status-of-the-art of and challenges for online privacy as well as derived recommendations for improving online privacy. Whereas the Dagstuhl Manifesto of this workshop concludes the results of the working groups and panel discussions, this article presents the talks of this workshop by their abstracts.
Dieser Tagungsband enthält die Beiträge des 17. Workshops „Computational Intelligence“ des Fachausschusses 5.14 der VDI/VDE-Gesellschaft für Mess- und Automatisierungstechnik (GMA) und der Fachgruppe „Fuzzy-Systeme und Soft-Computing“ der Gesellschaft für Informatik (GI), der vom 5. – 7. Dezember 2007 im Haus Bommerholz bei Dortmund stattfindet. Der GMA-Fachausschuss 5.14 „Computational Intelligence“ entstand 2005 aus den bisherigen Fachausschüssen „Neuronale Netze und Evolutionäre Algorithmen“ (FA 5.21) sowie „Fuzzy Control“ (FA 5.22). Der Workshop steht in der Tradition der bisherigen Fuzzy-Workshops, hat aber seinen Fokus in den letzten Jahren schrittweise erweitert. Die Schwerpunkte sind Methoden, Anwendungen und Tools für • Fuzzy-Systeme, • Künstliche Neuronale Netze, • Evolutionäre Algorithmen und • Data-Mining-Verfahren sowie der Methodenvergleich anhand von industriellen und Benchmark-Problemen. INHALTSVERZEICHNIS T. Fober, E. Hüllermeier, M. Mernberger (Philipps-Universität Marburg): Evolutionary Construction of Multiple Graph Alignments for the Structural Analysis of Biomolecules G. Heidemann, S. Klenk (Universität Stuttgart): Visual Analytics for Image Retrieval F. Rügheimer (OvG-Universität Magdeburg): A Condensed Representation for Distributions over Set-Valued Attributes T. Mrziglod (Bayer Technology Services GmbH, Leverkusen): Mit datenbasierten Technologien und Versuchsplanung zu erfolgreichen Produkten H. Schulte (Bosch Rexroth AG, Elchingen): Approximationsgenauigkeit und dynamisches Fehlerwachstum der Modellierung mit Takagi-Sugeno Fuzzy Systemen C. Burghart, R. Mikut, T. Asfour, A. Schmid, F. Kraft, O. Schrempf, H. Holzapfel, R. Stiefelhagen, A. Swerdlow, G. Bretthauer, R. Dillmann (Universität Karlsruhe, Forschungszentrum Karlsruhe GmbH): Kognitive Architekturen für humanoide Roboter: Anforderungen, Überblick und Vergleich R. Mikut, C. Burghart, A. Swerdlow (Forschungszentrum Karlsruhe GmbH, Universität Karlsruhe): Ein Gedankenexperiment zum Entwurf einer integrierten kognitiven Architektur für humanoide Roboter G. Milighetti, H.-B. Kuntze (FhG IITB Karlsruhe): Diskret-kontinuierliche Regelung und Überwachung von Robotern basierend auf Aktionsprimitiven und Petri-Netzen N. Rosemann, W. Brockmann (Universität Osnabrück): Kontrolle dynamischer Eigenschaften des Online-Lernens in Neuro-Fuzzy-Systemen mit dem SILKE-Ansatz A. Hans, D. Schneegaß, A. Schäfer, S. Udluft (Siemens AG, TU Ilmenau): Sichere Exploration für Reinforcement-Learning-basierte Regelung Th. Bartz-Beielstein, M. Bongards, C. Claes, W. Konen, H. Westenberger (FH Köln): Datenanalyse und Prozessoptimierung für Kanalnetze und Kläranlagen mit CI-Methoden S. Nusser, C. Otte, W. Hauptmann (Siemens AG, OvG-Universität Magdeburg): Learning Binary Classifiers for Applications in Safety-Related Domains W. Jakob, A. Quinte, K.-U. Stucky, W. Süß, C. Blume (Forschungszentrum Karlsruhe GmbH; FH Köln, Campus Gummersbach) Schnelles Resource Constrained Project Scheduling mit dem Evolutionären Algorithmus GLEAM M. Preuß, B. Naujoks (Universität Dortmund): Evolutionäre mehrkriterielle Optimierung bei Anwendungen mit nichtzusammenhängenden Pareto-Mengen G. Rudolph, M. Preuß (Universität Dortmund): in mehrkriterielles Evolutionsverfahren zur Bestimmung des Phasengleichgewichts von gemischten Flüssigkeiten Y. Chen, O. Burmeister, C. Bauer, R. Rupp, R. Mikut (Universität Karlsruhe, Forschungszentrum Karlsruhe GmbH, Orthopädische Universitätsklinik Heidelberg): First Steps to Future Applications of Spinal Neural Circuit Models in Neuroprostheses and Humanoid Robots F. Hoffmann, J. Braun, T. Bertram, S. Hölemann (Universität Dortmund, RWTH Aachen): Multikriterielle Optimierung mit modellgestützten Evolutionsstrategien S. Piana, S. Engell (Universität Dortmund): Evolutionäre Optimierung des Betriebs von rohrlosen Chemieanlagen T. Runkler (Siemens AG, CT IC 4): Pareto Optimization of the Fuzzy c–Means Clustering Model Using a Multi–Objective Genetic Algorithm H. J. Rommelfanger (J.W. Goethe-Universität Frankfurt am Main): Die Optimierung von Fuzzy-Zielfunktionen in Fuzzy (Mehrziel-) LPSystemen - Ein kritischer Überblick D. Gamrad, D. Söffker (Universität Duisburg-Essen): Formalisierung menschlicher Interaktionen durch Situations-Operator- Modellbildung S. Ritter, P. Bretschneider (FhG AST Ilmenau): Optimale Planung und Betriebsführung der Energieversorgung im liberalisierten Energiemarkt R. Seising (Medizinische Universität Wien): Heinrich Hertz, Ludwig Wittgenstein und die Fuzzy-Strukturen - Eine kleine „Bildergeschichte“ zur Erkenntnisphilosophie J. Limberg, R. Seising (Medizinische Universität Wien): Sequenzvergleiche im Fuzzy-Hypercube M. Steinbrecher, R. Kruse (OvG-Universität Magdeburg): Visualisierung temporaler Abhängigkeiten in Bayesschen Netzen M. Schneider, R. Tillmann, U. Lehmann, J. Krone, P. Langbein, U. Stark, J. Schrickel, Ch. Ament, P. Otto (FH Südwestfalen, Airbus Deutschland GmbH, Hamburg, TU Ilmenau): Künstliches Neuronales Netz zur Analyse der Geometrie von großflächig gekrümmten Bauteilen C. Frey (FhG IITB Karlsruhe): Prozessdiagnose und Monitoring feldbusbasierter Automatisierungsanlagen mittels selbstorganisierender Karten
Effort estimates are of utmost economic importance in software development projects. Estimates bridge the gap between managers and the invisible and almost artistic domain of developers. They give a means to managers to track and control projects. Consequently, numerous estimation approaches have been developed over the past decades, starting with Allan Albrecht's Function Point Analysis in the late 1970s. However, this work neither tries to develop just another estimation approach, nor focuses on improving accuracy of existing techniques. Instead of characterizing software development as a technological problem, this work understands software development as a sociological challenge. Consequently, this work focuses on the question, what happens when developers are confronted with estimates representing the major instrument of management control? Do estimates influence developers, or are they unaffected? Is it irrational to expect that developers start to communicate and discuss estimates, conform to them, work strategically, hide progress or delay? This study shows that it is inappropriate to assume an independency of estimated and actual development effort. A theory is developed and tested, that explains how developers and managers influence the relationship between estimated and actual development effort. The theory therefore elaborates the phenomenon of estimation fulfillment.
Plattformen für Social Communities im Internet, wie Facebook, StudiVZ und XING, haben in den vergangenen Jahren rasant an Popularität gewonnen. Auf ihnen versammeln sich bereits heute Millionen von Nutzern weltweit. Sie verbinden sich über virtuelle Freundeslisten und tauschen sich über gemeinsame Interessen und Aktivitäten aus. Immer häufger werden dazu auch mobile Endgeräte wie Handys verwendet, erlauben diese doch ständig in Kontakt mit der Community zu bleiben. Allerdings wollen viele Nutzer längst nicht jedem Mitglied einer Community alles preisgeben. Doch wie lässt sich die Privatsphäre in solchen Communities besser schützen? Dieser Frage geht das Forschungsprojekt PICOS nach.
This paper documents the experiences of assurance evaluation during the early stage of a large software development project. This project researches, contracts and integrates privacy-respecting software to business environments. While assurance evaluation with ISO 15408 Common Criteria (CC) within the certification schemes is done after a system has been completed, our approach executes evaluation during the early phases of the software life cycle. The promise is to increase quality and to reduce testing and fault removal costs for later phases of the development process. First results from the still-ongoing project suggests that the Common Criteria can define a framework for assurance evaluation in ongoing development projects.