George Roussos

Mohamed Ragab, Faria Ferooz, Mohammad Bahrani et al.

In decentralized personal data ecosystems grounded in architectures such as Solid, users retain sovereignty over their data via personal online data stores (pods), hosted on Solid-compliant server infrastructures. In such environments, data remains under the control of pod owners, which complicates search due to distribution across numerous pods and user-specific access constraints. ESPRESSO is a decentralized framework for scalable keyword-based search across distributed Solid pods under user-defined visibility policies. It addresses key challenges of decentralized search by constructing WebID-scoped indexes within pods and employing privacy-aware metadata to enable efficient source selection and ranking across servers. This paper further introduces a formal threat model for ESPRESSO, analysing the security and privacy risks associated with the generation, aggregation, and use of indexes and metadata. These risks include unintended metadata leakage and the potential for adversaries to infer sensitive information about data that resides within personal data stores. The analysis identifies key design principles that limit metadata exposure while mitigating unauthorized inference. The proposed threat model provides a foundation for evaluating privacy-preserving decentralized search and informs the design of systems with stronger privacy guarantees.

Eric Schneider, Marcus Poulton, Archie Drake et al.

Multi-Robot Task Allocation (MRTA) is the problem of distributing a set of tasks to a team of robots with the objective of optimising some criteria, such as minimising the amount of time or energy spent to complete all the tasks or maximising the efficiency of the team's joint activity. The exploration of MRTA methods is typically restricted to laboratory and field experimentation. There are few existing real-world models in which teams of autonomous mobile robots are deployed "in the wild", e.g., in industrial settings. In the work presented here, a market-based MRTA approach is applied to the problem of ambulance dispatch, where ambulances are allocated in respond to patients' calls for help. Ambulances and robots are limited (and perhaps scarce), specialised mobile resources; incidents and tasks represent time-sensitive, specific, potentially unlimited, precisely-located demands for the services which the resources provide. Historical data from the London Ambulance Service describing a set of more than 1 million (anonymised) incidents are used as the basis for evaluating the predicted performance of the market-based approach versus the current, largely manual, method of allocating ambulances to incidents. Experimental results show statistically significant improvement in response times when using the market-based approach.