Anonymization plays a key role in protecting sensible information of individuals in real world datasets. Self-driving cars for example need high resolution facial features to track people and their viewing direction to predict future behaviour and react accordingly. In order to protect people’s privacy whilst keeping important features in the dataset, it is important to replace the full body of a person with a highly detailed anonymized one. In contrast to doing face anonymization, full body replacement decreases the ability of recognizing people by their hairstyle or clothes. In this paper, we propose a workflow for full body person anonymization utilizing Stable Diffusion as a generative backend. Text-to-image diffusion models, like Stable Diffusion, OpenAI’s DALL-E or Midjourney, have become very popular in recent time, being able to create photorealistic images from a single text prompt. We show that our method outperforms state-of-the art anonymization pipelines with respect to image quality, resolution, Inception Score (IS) and Frechet Inception Distance (FID). Additionally, our method is invariant with respect to the image generator and thus able to be used with the latest models available.
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Market entrants have brought a variety of new urban mobility services over the past years, which are rooted in the sharing economy and have their origin in digitalization. Digital data serve as key resource of a business model and, accordingly, digital technologies are the basis of key activities. Building our analysis on the resource-based view and on the business model debate, we ask: what degrees of digitalization do urban mobility service business models exhibit? We perform a systematic literature review and a qualitative content analysis. As a result, we identify a continuum of highly and lowly digitalized business models. We derive a threefold business model framework, substantiated in conventional mobility, hybrid, and data-driven business models. (1) Conventional mobility business models are dominated by mobility as a key resource, digitalization is low and performed by key partners, (2) hybrid models contain both conventional mobility and data-driven key resources, and (3) data-driven models take digital data as key resources, while conventional mobility is carried out by key partners. As a first main contribution, we conceptualize conventional versus purely data-driven business models along the continuum of data-driven business model components. New urban mobility services are the group of both hybrid and data-driven business models, while conventional urban mobility stands on its own. As a second contribution, we clarify the Mobility-as-a-Service (MaaS) concept by corroborating it as a purely data-driven business model with key partners provisioning mobility.
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Sharing urban mobility and public transportation data is critical to use the mobility infrastructure of cities to its fullest potential. For data protection reasons, however, the disclosure of data to the public is restricted and only permitted if the anonymity of each individual associated with the dataset can be guaranteed. To achieve anonymity in a given dataset, numerous approaches can be applied, while each ap- proach follows a dierent denition of anonymity. One of the most used denitions is k-anonymity, which builds on the building of equivalence classes so that each row in a dataset belongs to an equivalence class that contains at least k rows that cannot be distinguished. Naturally, this can be achieved by multiple realizations. However, the question is which realization will provide the highest utility for future real-world applications. Currently, abstract metrics are used to assess the utility of dierent k-anonymizations, based on the structure of the dataset. However, these abstract metrics do not properly reect the usefulness of the anonymized datasets in real-world applications. Hence, in this work, we provide a novel framework that helps to evaluate the given abstract metrics from the literature in terms of their performance in measuring utility in the context of urban mobility. To do this, we de- ne a set of potential data science use cases that can be derived from a publicly available dataset on taxi drives and compute multiple real- izations of k-anonymity. By training prediction models on the original dataset and the anonymized datasets and comparing the corresponding performance decrease with the abstract metrics from the literature, we are able to derive recommendations on the usage of abstract metrics to evaluate the utility of potential realizations to achieve k-anonymity.
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In our modern world, the ever-expanding exchange of data and the increased complexity of interconnected software systems make software security challenging. Ideally, security concerns are already addressed early, as discussed with security by design. Here, architecture-based modeling enables the analysis of security threats such as confidentiality violations or targeted attacks. Such analyses leverage the structural properties of software architecture and information about the system context like deployment and usage. However, this requires security-specific model annotations and adapted propagation rules. In this paper, we present two architectural propagation analyses that extend the concept of change impact analysis for security analysis. Both analyses build on the architecture modeling language PCM. The former propagates uncertainty in software architectures and predicts its impact on the system’s confidentiality. The latter identifies attack paths using vulnerabilities and access control properties in attacker propagation.
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Digitalization is one of the biggest drivers of advancements in the modern automotive domain. The resulting increase in communication is leading to a more intensive exchange of data and the opening up of for merly closed systems. This raises questions about security and data protection. Software architecture analyses can help identify potential issues, thereby making systems more secure and compliant with data protection laws. Such analyses require representative case studies for development and evaluation. In this paper, we showcase the results of applying requirements and processes for case-study research during three bachelor theses with students. The resulting three case studies center around the automotive and mobility domain and focus on different security and privacy properties. We discuss our insights and experiences regarding the creation of case studies.
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The transformation of mobility is on the cusp of a significant shift,driven by data-centric technologies in both individual and public transport. However, this data often contains sensitive private data, which can be used, for instance, for tracking a person. Hence, anonymization of this mobility data is important. In this report, we present a structured literature review about anonymization methods in the mobility domain. Based on our findings, we present different anonymization methods and discuss their application scenarios and characteristics for public transport and individual one. Additionally, an industry-driven survey on anonymization methods within public transport, particularly centered around video technologies, is presented. This industry-driven survey, conducted within a video surveillance solutions company, highlights current trends and underscores the necessity for continued research. This report was created within the ANYMOS project.
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Analyzing attacks and potential attack paths can help to identify and avoid potential security incidents. Manually estimating an attack path to a targeted software element can be complex since a software system consists of multiple vulnerable elements, such as components, hardware resources, or network elements. In addition, the elements are protected by access control. Software architecture describes the structural elements of the system, which may form elements of the attack path. However, estimating attack paths is complex since different attack paths can lead to a targeted element. Additionally, not all attack paths might be relevant since attack paths can have different properties based on the attacker’s capabilities and knowledge. We developed an approach that enables architects to identify relevant attack paths based on the software architecture. We created a metamodel for filtering options and added support for describing attack paths in an architectural description language. Based on this metamodel, we developed an analysis that automatically estimates attack paths using the software architecture. This can help architects to identify relevant attack paths to a targeted component and increase the system’s overall security. We evaluated our approach on five different scenarios. Our evaluation goals are to investigate our analysis’s accuracy and scalability. The results suggest a high accuracy and good runtime behavior for smaller architectures.
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In ANYMOS werden Anforderungen und Methoden für eine Anonymisierung und anschließende Auswertung von zuvor personenbezogenen Daten untersucht. Dabei wird im Kompetenzcluster die Anwendungsdomäne Mobilität betrachtet und sich auf den Personenverkehr, da durch die Mobilität von Gütern nicht immer unmittelbar personenbezogene Daten anfallen, fokussiert. Die Notwendigkeit des Kompetenzclusters ANYMOS ergibt sich daraus, dass im Mobilitätsbereich bei zahlreichen Anwendungen große Datenmengen anfallen und es aufgrund der zu erwartenden Entwicklungen zu einem weiteren Anstieg dieser Datenmenge kommen wird. Um diese Daten in Zukunft sinnvoll nutzen zu können, ohne dabei durch die Verwendung personenbezogener Daten Persönlichkeitsrechte und/oder rechtliche Vorgaben zu verletzen, muss zunächst erforscht werden, wann diese Daten gesammelt werden und inwieweit sie auch nach einer Anonymisierung noch über einen Nutzwert verfügen.
Im zweiten Abschnitt des Whitepapers wird daher zunächst Anonymität beschrieben und das Spannungsfeld zwischen juristischem und technischen Begriffsverständnis erörtert.
Im dritten Abschnitt erfolgt eine Strukturierung der Mobilitätsdomäne. Dadurch soll das gemeinsame Verständnis der Begrifflichkeiten und der Relevanz der verschiedenen Themenbereiche für das Kompetenzcluster ANYMOS gefördert werden.
Abschließend wird ein Ausblick – auch auf die weiteren Arbeiten in ANYMOS gegeben.
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Die betriebswirtschaftliche Literatur identifiziert Geschäftsmodelle als ein entscheidendes Element von Unternehmen, das ihnen im Wettbewerb Vorteile verschafft. Der vorliegende Beitrag geht den Fragen nach: Welche sind die Trends, die Geschäftsmodellen vor dem Hintergrund der Digitalisierung zugrunde liegen, und was sind kennzeichnende Charakteristika von digitalen Geschäftsmodellen? Hierzu wird zunächst anhand der identifizierten Literatur nachgezeichnet, was digitale Geschäftsmodelle sind und was Geschäftsmodellinnovation ausmacht. Weiterhin werden Digitalisierungstrends aufgeführt, die einen Einfluss auf die Gestaltung von Geschäftsmodellen haben. Es zeigt sich, dass die Hybridisierung von Produkten die Logik der digitalen Welt in die physische Welt trägt. Außerdem kommt der Nutzerin und dem Nutzer eine zunehmend zentrale Rolle zu: Sie oder er nimmt die dreifache Rolle der Kundin oder des Kunden, des Datenbeitragenden und des Produkts ein. Ein zu beachtender Bereich ist dabei die Datensouveränität der Nutzerin und des Nutzers, deren Bedeutung in der Literatur zunehmend diskutiert wird. Schließlich rücken bei der Digitalisierung Preis- und Qualitätsmerkmale in den Hintergrund, der Zugang zur Kundin und zum Kunden findet vielmehr über eine Identitätsleistung des Anbieters statt. Digitale Geschäftsmodelle weisen die folgenden drei kennzeichnenden Charakteristika auf: (1) Eine Integration von Nutzerinnen und Nutzern sowie Kundinnen und Kunden, (2) eine Dienstleistungsorientierung und (3) die Kernkompetenz der Anbieter auf große Datenmengen, also Analytics von Big Data. Der Beitrag führt zu jedem der drei Charakteristika Beispiele für digitale Geschäftsmodelle auf.
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Through the increasing interconnection between various systems, the need for confidential systems is increasing. Confidential systems share data only with authorized entities. However, estimating the confidentiality of a system is complex, and adjusting an already deployed software is costly. Thus, it is helpful to have confidentiality analyses, which can estimate the confidentiality already at design time. Based on an existing data-flow-based confidentiality analysis concept, we reimplemented a data flow analysis as a Java-based tool. The tool uses the software architecture to identify access violations based on the data flow. The evaluation for our tool indicates that we can analyze similar scenarios and scale for certain scenarios better than the existing analysis.
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