Abstract
Conflict-free Replicated Data Types (CRDTs) are a very popular class of distributed data structures that strike a compromise between strong and eventual consistency. Ensuring the protection of data stored within a CRDT, however, cannot be done trivially using standard encryption techniques, as secure CRDT protocols would require replica-side computation. This paper proposes an approach to lift general-purpose implementations of CRDTs to secure variants using secure multiparty computation (MPC). Each replica within the system is realized by a group of MPC parties that compute its functionality. Our results include: i) an extension of current formal models used for reasoning over the security of CRDT solutions to the MPC setting; ii) a MPC language and type system to enable the construction of secure versions of CRDTs and; iii) a proof of security that relates the security of CRDT constructions designed under said semantics to the underlying MPC library. We provide an open-source system implementation with an extensive evaluation, which compares different designs with their baseline throughput and latency.

Abstract
The application of machine learning to insurance risk prediction requires learning from sensitive data. This raises multiple ethical and legal issues. One of the most relevant ones is privacy. However, privacy-preserving methods can potentially hinder the predictive potential of machine learning models. In this paper, we present preliminary experiments with life insurance data using two privacy-preserving techniques: discretization and encryption. Our objective with this work is to assess the impact of such privacy preservation techniques in the accuracy of ML models. We instantiate the problem in three general, but plausible Use Cases involving the prediction of insurance claims within a 1-year horizon. Our preliminary experiments suggest that discretization and encryption have negligible impact in the accuracy of ML models.

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Abstract
We consider global models of communicating agents specified as transition systems labelled by interactions in which multiple senders and receivers can participate. A realisation of such a model is a set of local transition systems—one per agent—which are executed concurrently using synchronous communication. Our core challenge is how to check whether a global model is realisable and, if it is, how to synthesise a realisation. We identify and compare two variants to realise global interaction models, both relying on bisimulation equivalence. Then we investigate, for both variants, realisability conditions to be checked on global models. We propose a synthesis method for the construction of realisations by grouping locally indistinguishable states. The paper is accompanied by a tool that implements realisability checks and synthesises realisations. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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[No abstract available]