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
Quattrocchi, Giovanni;Plebani, Pierluigi
2024-01-01
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.