Abstract Runtime enforcers receive events from a system and output commands ensuring the system’s policy compliance. Proactive enforcers extend traditional (reactive) enforcers by emitting commands at any time, rather only as a response to system actions. However, proactive enforcers have so far lacked support for many useful policy features. This, along with the existing tools’ poor performance, hinders their adoption. We present a performance-optimized, proactive enforcement algorithm for a rich policy language: metric first-order temporal logic with function applications, aggregations, and bindings. We have implemented this algorithm in EnfGuard , the first proactive enforcer tool that supports the above constructs. We evaluated our tool using a novel set of six benchmarks containing both real-world and synthetic policies and logs, demonstrating that it enforces realistic policies out-of-the-box and achieves the necessary performance to be used in real-time systems.

Privacy by design requires integrating data protection into systems from the outset, during their design, rather than building it in later. Related legislation does not specify how to achieve this and mainstream languages and frameworks lack support for privacy by design. To address this long-standing problem, we have developed different, effective technical solutions. First, we have developed powerful logic-based tools that enforce formal data protection policies at runtime by controlling relevant system actions. Second, we have proposed methods and tools for integrating privacy models into system design models, enabling model-driven privacy enforcement. We report on our methods, tools, and practical experiences using them.

Abstract Modern software systems must comply with increasingly complex regulations in domains ranging from industrial automation to data protection. Runtime enforcement addresses this challenge by empowering systems to not only observe, but also actively control, the behavior of target systems by modifying their actions to ensure policy compliance. We propose a novel approach to the proactive real-time enforcement of policies expressed in metric first-order temporal logic (MFOTL). We introduce a new system model, define an expressive MFOTL fragment that is enforceable in that model, and develop a sound enforcement algorithm for this fragment. We implement this algorithm in a tool calledWhyEnfand carry out a case study on enforcing GDPR-related policies. Our tool can enforce all policies from the study in real-time with modest overhead. Our work thus provides the first tool-supported approach that can proactively enforce expressive first-order policies in real time.

We develop the first language-based, Privacy by Design approach that provides support for a rich class of privacy policies. The policies are user-defined, rather than programmer-defined, and support fine-grained information flow restrictions (considering individual application inputs and outputs) with temporal constraints. Our approach, called Taint, Track, and Control (TTC), combines dynamic information-flow control and runtime verification to enforce these policies in the presence of malicious users and developers. We provide TTC's semantics and proofs of its correct enforcement, formalized in the Isabelle/HOL proof assistant. We also implement our approach in a web development framework and port three baseline applications from previous work into this framework for evaluation. Overall, our approach enforces expressive user-defined privacy policies with practical runtime performance.