Today's emerging Industrial Internet of Things (IIoT) scenarios are characterized by the exchange of data between services across enterprises. Traditional access and usage control mechanisms are only able to determine if data may be used by a subject, but lack an understanding of how it may be used. The ability to control the way how data is processed is however crucial for enterprises to guarantee (and provide evidence of) compliant processing of critical data, as well as for users who need to control if their private data may be analyzed or linked with additional information -a major concern in IoT applications processing personal information. In this paper, we introduce LUCON, a data-centric security policy framework for distributed systems that considers data flows by controlling how messages may be routed across services and how they are combined and processed. LUCON policies prevent information leaks, bind data usage to obligations, and enforce data flows across services. Policy enforcement is based on a dynamic taint analysis at runtime and an upfront static verification of message routes against policies. We discuss the semantics of these two complementing enforcement models and illustrate how LUCON policies are compiled from a simple policy language into a first-order logic representation. We demonstrate the practical application of LUCON in a realworld IoT middleware and discuss its integration into Apache Camel. Finally, we evaluate the runtime impact of LUCON and discuss performance and scalability aspects.

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Security and privacy by design are important paradigms for establishing high protection levels in the eHealth domain. This means that security requirements and privacy concerns are considered and analyzed from the very beginning of any system design. For a reliable and robust system architecture and specifi cation we recommend a four-step approach: (1) Decompose the system and identify the assets on the basis of the multilateral security concept, i.e., taking all participants of an eHealth scenario as potential attackers into account; (2) evaluate threats based on STRIDE for a holistic and systematic modelling of threats; (3) defi ne use casespecifi c security requirements and privacy concerns as well as their relevance; and (4) mitigate threats by deciding what countermeasures should be implemented. After the introduction of each step this chapter illustrates the practical use in a stepby-step walkthrough with a real-world eHealth scenario and discusses advantages of security and privacy by design as well as its limitations.

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