I Did Not Accept That: Demonstrating Consent in Online Collection of Personal Data

Privacy in online collection of personal data is currently a much debated topic considering, amongst other reasons, the incidents with well known digital organisations, such as social networks and, in Europe, the recent EU/GDPR regulation. Among other required practices, explicit and simply worded consent from individuals must be obtained before collecting and using personal information. Further, individuals must also be given detailed information about what, how and what for data is collected. Consent is typically obtained at the collection point and, at a single point in time (ignoring updates), associated with Privacy Policies or End-User Agreements. At any moment, both the user and the organization should be able to produce evidence of this consent. This proof should not be disputable which leads us to strong cryptographic properties. The problem we discuss is how to robustly demonstrate such consent was given. We adapt fair-exchange protocols to this particular problem and, upon an exchange of personal data, we are able to produce a cryptographic receipt of acceptance that any party can use to prove consent and elicit non-repudiation. We discuss two broad strategies: a pure peerto-peer scheme and the use of a Trusted Third Party

Utilising Assured Multi-Agent Reinforcement Learning within safety-critical scenarios

Multi-agent reinforcement learning allows a team of agents to learn how to work together to solve complex decision-making problems in a shared environment. However, this learning process utilises stochastic mechanisms, meaning that its use in safety-critical domains can be problematic. To overcome this issue, we propose an Assured Multi-Agent Reinforcement Learning (AMARL) approach that uses a model checking technique called quantitative verification to provide formal guarantees of agent compliance with safety, performance, and other non-functional requirements during and after the reinforcement learning process. We demonstrate the applicability of our AMARL approach in three different patrolling navigation domains in which multi-agent systems must learn to visit key areas by using different types of reinforcement learning algorithms (temporal difference learning, game theory, and direct policy search). Furthermore, we compare the effectiveness of these algorithms when used in combination with and without our approach. Our extensive experiments with both homogeneous and heterogeneous multi-agent systems of different sizes show that the use of AMARL leads to safety requirements being consistently satisfied and to better overall results than standard reinforcement learning