Authors: Marco Anisetti, Claudio A. Ardagna, Filippo Berto

Contacts: {firstname.lastname}@unimi.it

Modern (industrial) domains are based on large digital ecosystems where huge amounts of data and information need to be collected, shared, and analyzed by multiple actors working within and across organizational boundaries. This data-driven ecosystem poses strong requirements on data management and data analysis, as well as on data protection and system trustworthiness. However, although Big Data has reached its functional maturity and represents a key enabler for enterprises to compete in the global market, the assurance and trustworthiness of Big Data computations (e.g., security, privacy) is still in its infancy. While functionally appealing, Big Data does not provide a transparent environment with clear non-functional properties, impairing the users’ ability to evaluate its behavior and clashing with modern data-privacy regulations. In this paper, we present an enhanced assurance process for Big Data, which aims to increase transparency and trustworthiness of Big Data computations. The assurance process evaluates Big Data computations at all layers from the specific big data pipelines to the big data ecosystem underneath.