The increasing connectivity and data exchange between vehicles and the cloud have led to growing privacy concerns. To keep on gaining product insights through data collection while guaranteeing privacy protection, an anonymization-by-design approach should be used. A rising number of anonymization methods, not limited to the automotive domain, can be found in the literature and practice. The developers need support to select the suitable anonymization technique. To this end, we make the following two contributions: 1) We apply our knowledge from the automotive domain to outline the usage of qualitative metrics for anonymization techniques assessment; 2) We introduce HySAAD, a hybrid selection approach for anonymization by design that leverages this groundwork by recommending appropriate anonymization techniques for each mobile data analytics use case based on both, qualitative (i.e., "soft") metrics and quantitative (i.e., "hard") metrics. Using a real-world use case from the automotive, we demonstrate the applicability and effectiveness of HySAAD.

The extensive use of onboard sensors in smart cars enables the collection, processing, and dissemination of large amounts of mobile data containing information about the vehicle, its driver, and even bystanders. Despite the undoubted benefits of such smart cars, this leads to significant privacy concerns. Due to their inherent mobility, the situation of smart cars changes frequently, and with it, the appropriate measures to counteract the exposure of private data. However, data management in such vehicles lacks sufficient support for this privacy dynamism. We therefore introduce PaDS, a framework for Privacy adaptive Data Stream. The focus of this paper is to enable adaptive data processing within the vehicle data stream. With PaDS, Privacy-Enhancing Technologies can be deployed dynamically in the data pipeline of a smart car according to the current situation without user intervention. With a comparison of state-of-the-art approaches, we demonstrate that our solution is very efficient as it does not require a complete restart of the data pipeline. Moreover, compared to a static approach, PaDS causes only minimal overhead despite its dynamic adaptation of the data pipeline to react to changing privacy requirements. This renders PaDS an effective privacy solution for smart cars.

Data has never been as significant as it is today. It can be acquired virtually at will on any subject. Yet, this poses new challenges towards data management, especially in terms of storage (data is not consumed during processing, i. e., the data volume keeps growing), flexibility (new applications emerge), and operability (analysts are no IT experts). The goal has to be a demand-driven data provisioning, i. e., the right data must be available in the right form at the right time. Therefore, we introduce a tailorable data preparation zone for Data Lakes called BARENTS. It enables users to model in an ontology how to derive information from data and assign the information to use cases. The data is automatically processed based on this model and the refined data is made available to the appropriate use cases. Here, we focus on a resource-efficient data management strategy. BARENTS can be embedded seamlessly into established Big Data infrastructures, e. g., Data Lakes.

\n The Internet of Things is applied in many domains and collects vast\n amounts of data. This data provides access to a lot of knowledge\n when analyzed comprehensively. However, advanced analysis techniques\n such as predictive or prescriptive analytics require access to both,\n history data, i.e., long-term persisted data, and real-time data as\n well as a joint view on both types of data. State-of-the-art hybrid\n processing architectures for big data - namely, the Lambda and the\n Kappa Architecture - support the processing of history data and\n real-time data. However, they lack of a tight coupling of the two\n processing modes. That is, the user has to do a lot of work manually\n in order to enable a comprehensive analysis of the data. For\n instance, the user has to combine the results of both processing\n modes or apply knowledge from one processing mode to the other.\n Therefore, we introduce a novel hybrid processing architecture for\n big data, called BRAID. BRAID intertwines the processing of history\n data and real-time data by adding communication channels between the\n batch engine and the stream engine. This enables to carry out\n comprehensive analyses automatically at a reasonable overhead.\n

Due to rising medical costs, the healthcare landscape is on the move. Novel treatment methods are badly required. Especially for the treatment of chronic diseases the usage of smart devices in combination with medical devices for telemedical screenings is a promising approach. If the patients are not in control of the collection and processing of their health data, privacy concerns limit their willingness to use such a method. In this paper, we present a prototype for an Android-based privacy-aware health game for children suffering from diabetes called Secure Candy Castle. In the game, the player keeps an electronic diabetes diary in a playful manner. In doing this, s/he is supported by various sensors. His or her data is analyzed and in case of a critical health condition, the game notifies authorized persons. With our approach, the user stays in control over his or her data, i.e., s/he defines which data should be shared with the game, how accurate this data should be, and even how the data is processed by the game. For this purpose, we apply the Privacy Management Platform, a fine-grained and extendable permission system.

Smart devices became Marc Weiser's Computer of the 21st Century. Due to their versatility a lot of private data enriched by context data are stored on them. Even the health industry utilizes smart devices as portable health monitors and enablers for telediagnosis. So they represent a severe risk for information security. Yet the platform providers' countermeasures to these threats are by no means sufficient. In this paper we describe how information security can be improved. Therefore, we postulate requirements towards a secure handling of data. Based on this requirements specification, we introduce a secure data container as an extension for the Privacy Management Platform. Since a complete isolation of an app is usually not practicable, our approach also provides secure data sharing features. Finally, we evaluate our approach from a technical point of view as well as a security point of view and show its applicability in an eHealth scenario.

Nowadays, mobile devices collect a lot of private information. Therefore every vendor of a mobile platform has to provide a sufficient mechanism to secure this data. Android pursues a strategy to pass full control (and thus full responsibility) over any private data to the user. However, the Android Permission System is not sufficient for that purpose. Various third party approaches try to enhance the Android privacy policy model. Nevertheless, these approaches have to solve the problem of how to deal with Legacy Apps, i.e., Apps that do not collaborate with an enhanced privacy policy model. In this paper, we analyze various alternative privacy systems and discuss different approaches of how to deal with Legacy Apps. Based on our findings, we introduce the so-called PMP Gatekeeper, a best of breed approach dealing with Legacy Apps for the Privacy Management Platform (PMP). The PMP Gatekeeper classifies apps and deals with each class appropriately. So the user can adjust privacy settings for every kind of app. With our prototype we show, that the PMP in combination with the PMP Gatekeeper becomes a holistic privacy system. Although our prototype is for Android, our realization approach can be applied to other application platforms in order to offer a satisfying privacy system.

Nowadays, mobile platform vendors have to concern themselves increasingly about how to protect their users' privacy. As Google is less restrictive than their competitors regarding their terms of use for app developers, it is hardly surprising that malware spreads even in Google Play. To make matters worse, in Android every user is responsible for his or her private data and s/he is frequently overwhelmed with this burden because of the fragile Android permission mechanism. Thus, the calls for a customizable, fine-grained, context-based, crash-proof, and intuitive privacy management system are growing louder. To cope with these requests, we introduce the Privacy Management Platform (PMP) and we discuss three alternative implementation strategies for such a system.

There is an increasing popularity of mobile devices-especially Android devices-particularly because of the huge amount of available third-party applications. Albeit, the number of diagnosed hacker attacks against mobile user increased in unison, as these devices became the prime target of the latest malware, thanks to inexperienced users and a negligent way of dealing with private data. To make matters worse, the Android permission system is much too coarse-grained and too hard to grasp for an average user. However, even if a user is able to comprehend the meaning and impact of a certain permission, in the end, s/he must grant all requested permission anyhow, if s/he wants to install the application. Therefore, we introduce PMP a privacy management platform for Android, which enables a user to grant an application fine-grained access rights to critical data. Furthermore, those rights can depend on some contextual constraints (e.g. Internet usage is restricted to certain locations) and the policy rules can be modified at run-time. Depending upon the granted rights an application provides a different scope of service. Moreover, the user is-due to a catchy GUI-always informed what impact the granting or respectively the revocation of a permission has on the application's service quality.

The still rising popularity of modern mobile phones results in an increased demand for manifold applications for these devices. As Android OS supports the development and usage of third-party software greatly, there are more and more developers for this platform. However, many of those applications handle private data grossly negligent which immediately leads to serious privacy concerns. To make matters worse, the current Android permission rules are much too coarse and incomprehensible from the average user's perspective. But even if s/he understands the meaning of the permissions, s/he must either accept all of them or waive the application. Therefore we review concepts and approaches towards effective privacy management for mobile platforms. All this is discussed based on the prevailing key players in the mobile market, namely Apple, RIM, Microsoft and Google. As this work has been initiated by Google we mainly concentrated on Android-based concepts towards customizable privacy management approaches. As a result of our review and taking into account current initiatives and trends in the market, we come up with a novel approach, an implementation architecture and a prototype.

In times of lacking natural resources and increasing environmental pollution at the same time, modern resource efficient kinds of personal transportation have to be considered. Ride-sharing is maybe one of the most economical ways to avouch permanent mobility without losing too much comfort. However, especially dynamic ride-sharing is laden with many resistances including a lack of security and a heavy scheduling and coordinating burden. Hence this paper introduces an implementation of a system for dynamic ride-sharing called vHike which should eliminate these barriers. With our demonstrator every interested participant may test whether or not such a system can be viable and effective.