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

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.

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
The new energy policies adopted by the European Union are set to help in the decarbonization of the energy system. In this context, the share of Variable Renewable Energy Sources is growing, affecting electricity markets, and increasing the need for system flexibility to accommodate their volatility. For this reason, legislation and incentives are being developed to engage consumers in the power sector activities and in providing their potential flexibility in the scope of grid system services. This work identifies energy and cross-sector Business Models (BM) centered on or linked to the provision of distributed flexibility to the DSO and TSO, building on those found in previous research projects or from companies' commercial proposals. These BM are described and classified according to the main actor. The remaining actors, their roles, the interactions among them, how value is created by the BM activities and their value propositions are also described.