Trustworthy Collaborative Business Intelligence Using Zero-Knowledge Proofs and Blockchains
Lecture Notes in Business Information Processing, ISSN: 1865-1356, Vol: 520 LNBIP, Page: 29-37
2024
- 8Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Captures8
- Readers8
Conference Paper Description
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.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85195557388&origin=inward; http://dx.doi.org/10.1007/978-3-031-61000-4_4; https://link.springer.com/10.1007/978-3-031-61000-4_4; https://dx.doi.org/10.1007/978-3-031-61000-4_4; https://link.springer.com/chapter/10.1007/978-3-031-61000-4_4
Springer Science and Business Media LLC
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