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.
2024
Intelligent Information Systems. CAiSE 2024.
9783031609992
9783031610004
collaborative business intelligence
zero-knowledge proofs
data exchange
data warehouse
data sharing
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1276525
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