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.| File | Dimensione | Formato | |
|---|---|---|---|
|
978-3-031-61000-4_4.pdf
accesso riservato
Tipologia:
Publisher's version/PDF
Licenza:
Nessuna licenza
Dimensione
218.82 kB
Formato
Adobe PDF
|
218.82 kB | Adobe PDF | Visualizza/Apri Richiedi una copia |
Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.




