Abstract
This paper presents a data market aimed at trading energy forecasts data. The system architecture is built using blockchain as a service, allowing access to data streams and establishing a distributed settlement between stakeholders. Energy Forecasts data is presented as the commodity traded in the market, whose settlement is provided through the blockchain on the basis of the extracted value provided by market stakeholders. Our proposal allows market stakeholders to acquire energy forecasts and pay according to the data accuracy, solving the confidentiality problem of freely sharing data. A data quality reward is introduced, steering the compensation sent to market participants. The data market design is presented and an evaluation campaign is performed, showing that the data market produced functionally valid results in comparison with the results achieved with a central simulated approach. Moreover, results show that the data market architecture is able to scale.

Abstract
As blockchains go beyond cryptocurrencies into applications in multiple industries such as Insurance, Healthcare and Banking, handling personal or sensitive data, data access control becomes increasingly relevant. Access control mechanisms proposed so far are mostly based on requester identity, particularly for permissioned blockchain platforms, and are limited to binary, all-or-nothing access decisions. This is the case with Hyperledger Fabric's native access control mechanisms and, as permission updates require consensus, these fall short regarding the flexibility required to address GDPR-derived policies and client consent management. We propose SDAM, a novel access control mechanism for Fabric that enables fine-grained and dynamic control policies, using both contextual and resource attributes for decisions. Instead of binary results, decisions may also include mandatory data transformations as to conform with the expressed policy, all without modifications to Fabric. Results show that SDAM's overhead w.r.t baseline Fabric is acceptable. The scalability of the approach w.r.t to the number of concurrent clients is also evaluated and found to follow Fabric's.

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. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract

Abstract

Abstract
Construction and analysis of distributed systems is difficult. Multiparty session types (MPST) constitute a method to make it easier. The idea is to use type checking to statically prove deadlock freedom and protocol compliance of communicating processes. In practice, the premier approach to apply the MPST method in combination with mainstream programming languages has been based on API generation. In this paper (pearl), we revisit and revise this approach. Regarding our “revisitation”, using Scala 3, we present the existing API generation approach, which is based on deterministic finite automata (DFA), in terms of both the existing states-as-classes encoding of DFAs as APIs, and a new states-as-type-parameters encoding; the latter leverages match types in Scala 3. Regarding our “revision”, also using Scala 3, we present a new API generation approach that is based on sets of pomsets instead of DFAs; it crucially leverages match types, too. Our fresh perspective allows us to avoid two forms of combinatorial explosion resulting from implementing concurrent subprotocols in the DFA-based approach. We implement our approach in a new API generation tool. © Guillermina Cledou, Luc Edixhoven, Sung-Shik Jongmans, and Jos Proena; licensed under Creative Commons License CC-BY 4.0