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

Abstract

Abstract
Transactional isolation is a challenge for polystores, as along with the limited capabilities of each datastore, we have to contend with their sheer diversity. However, transactional isolation is increasingly desirable as a variety of datastores are being sought after for roles that go beyond data lakes. Transactional guarantees are also relevant for reliability at scale. In this paper, we propose that transactional isolation in polystores can be achieved by leveraging the query engine, i.e., basing some of the responsibilities of a traditional transactional storage manager (TSM) on the query language itself. This has the key advantage of greatly simplifying design and implementation, as it doesn’t need to be re-invented for each datastore, and should increase performance, by taking advantage of dynamic query optimization where available. We demonstrate the feasibility of the proposal with a simple proof-of-concept and experiment. © 2021, Springer Nature Switzerland AG.