Abstract. Usage control is concerned with what happens to data after access has been granted. In the literature, usage control models have been defined on the grounds of events that, somehow, are related to data. In order to better cater to the dimension of data, we extend a usage control model by the explicit distinction between data and representation of data. A data flow model is used to track the flow of data in-between different representations. The usage control model is then extended so that usage control policies can address not just one single representation (e.g., delete file1.txt after thirty days) but rather all representations of the data (e.g., if file1.txt is a copy of file2.txt, also delete file2.txt). We present three proof-of-concept implementations of the model, at the operating system level, at the browser level, and at the X11 level, and also provide an ad-hoc implementation for cross-layer enforcement.

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Using data flow tracking technology, one can observe how data flows from inputs (sources) to outputs (sinks) of a software system. It has been proposed [1] to do runtime data flow tracking at various layers simultaneously (operating system, application, data base, window manager, etc.), and connect the monitors' observations to exploit semantic information about the layers to make analyses more precise. This has implications on performance-multiple monitors running in parallel-and on methodology-there needs to be one dedicated monitor per layer. We address both aspects of the problem. We replace a runtime monitor at a layer L by its statically computed input-output dependencies. At runtime, these relations are used by monitors at other layers to model flows of data through L, thus allowing cross-layer system-wide tracking. We achieve this in three steps: (1) static analysis of the application at layer L, (2) instrumentation of the application's source and sink instructions and (3) runtime execution of the instrumented application in combination with monitors at other layers. The result allows for system-wide tracking of data dissemination, across and through multiple applications. We implement our solution at the Java Bytecode level, and connect it to a runtime OS-level monitor. In terms of precision and performance, we outperform binary-level approaches and can exploit high-level semantics.