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These days, sensitive and personal information is used within a wide range of applications. The exchange of this information is increasingly faster and more and more unpredictable. Hence, the person concerned cannot determine what happens with his personal data after it has been released. It is highly intransparent who is accountable for data misuse. Usage control and provenance tracking are two different approaches to tackle this problem. This work compares the two concepts from a data protection perspective. The support and fulfillment of data protection requirements are analysed. Models and architectures are investigated for commonalities. Combining the two technologies can increase flexibility and effectiveness of provenance tracking and thereby enhance information accountability in practice, if resulting linkability drawbacks are properly handled. A joint architecture is proposed to support this insight.

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Modern surveillance systems collect a massive amount of data. In contrast to conventional systems that store raw sensor material, modern systems take advantage of smart sensors and improvements in image processing. They extract relevant information about the observed objects of interest, which is then stored and processed during the surveillance process. Such high-level information is, e.g., used for situation analysis and can be processed in different surveillance tasks. Modern systems have become powerful, can potentially collect all kind of user information and make it available to any surveillance task. Hence, direct access to the collected high-level data must be prevented. Multiple approaches for anonymization exist, but they do not consider the special requirements of surveillance tasks. This work examines and evaluates existing metrics for anonymization and approaches for anonymization. Even though all kinds of data can be collected, position data is still the one with the highest demand. Hence, this work focuses on the anonymization of position data and proposes an algorithm that fulfills the requirements for anonymization in surveillance.

Part 8: Identifiability and Decision MakingInternational audienceThe protection of personal identifiable information (PII) is increasingly demanded by customers and data protection regulation. To safeguard PII a organization has to find out which incoming communication actually contains it. Only then PII can be labeled, tracked, and protected. E-mails are one of the main means of communication. They consist of unstructured data difficult to classify. We developed an automated detection system for PII in e-mails and connected it to a usage control infrastructure. Our concept is based on previous findings in the area of spam detection. We tested our approach with a data set in a customer service scenario. The evaluation shows that the utilization of Bayes-classification is very promising to detect PII