Abstract
Motivated by our experience with the Bank of Italy, in this work we present Vada-SA, a reasoning framework for financial data exchange with statistical confidentiality. By reasoning on the interplay of the features that may lead to identity disclosure, the framework is able to guarantee explainable, declarative, and context-aware confidentiality.
The views and opinions expressed in this paper are those of the authors and do not necessarily reflect the official policy or position of Banca d’Italia.
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Bellomarini, L., Blasi, L., Laurendi, R., Sallinger, E. (2021). A Reasoning Approach to Financial Data Exchange with Statistical Confidentiality. In: Kamp, M., et al. Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2021. Communications in Computer and Information Science, vol 1525. Springer, Cham. https://doi.org/10.1007/978-3-030-93733-1_16
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