In the era of data-driven decision-making, the ability to securely and reliably exchange analytical data among organizations (collaborative business intelligence) is becoming increasingly important. This paper envisions a novel framework for trustworthy data exchange, leveraging Zero-Knowledge Proofs (ZK-Proofs) to maintain data privacy and integrity, and the blockchain for reliable auditing. Our framework emphasizes enhancing business intelligence capabilities through non-operational analytics, particularly in the generation of aggregated insights for strategic decision-making among different organizations, without exposing the underlying raw data, thus preserving data sovereignty. We introduce a methodology to perform operations on data cubes using ZK-Proofs, allowing for the generation of more aggregated data cubes from initial raw data hypercubes. The framework exploits the Data-Fact Model to identify the available transformation paths on raw data.

Trustworthy Collaborative Business Intelligence Using Zero-Knowledge Proofs and Blockchains / G. Quattrocchi, P. Plebani (LECTURE NOTES IN BUSINESS INFORMATION PROCESSING). - In: Intelligent Information Systems / [a cura di] S. Islam, A. Sturm. - [s.l] : Springer, 2024. - ISBN 9783031609992. - pp. 29-37 (( CAiSE Limassol 2024 [10.1007/978-3-031-61000-4_4].

Trustworthy Collaborative Business Intelligence Using Zero-Knowledge Proofs and Blockchains

G. Quattrocchi
Primo
;
2024

Abstract

In the era of data-driven decision-making, the ability to securely and reliably exchange analytical data among organizations (collaborative business intelligence) is becoming increasingly important. This paper envisions a novel framework for trustworthy data exchange, leveraging Zero-Knowledge Proofs (ZK-Proofs) to maintain data privacy and integrity, and the blockchain for reliable auditing. Our framework emphasizes enhancing business intelligence capabilities through non-operational analytics, particularly in the generation of aggregated insights for strategic decision-making among different organizations, without exposing the underlying raw data, thus preserving data sovereignty. We introduce a methodology to perform operations on data cubes using ZK-Proofs, allowing for the generation of more aggregated data cubes from initial raw data hypercubes. The framework exploits the Data-Fact Model to identify the available transformation paths on raw data.
collaborative business intelligence; zero-knowledge proofs; data exchange; data warehouse; data sharing
Settore IINF-05/A - Sistemi di elaborazione delle informazioni
Settore INFO-01/A - Informatica
2024
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/1227058
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