Preserving data privacy and confidentiality in a distributed ledger : a use case in logistics

Permissioned Blockchains have been proposed and assessed as a novel and promising approach for improving data sharing, trust, and integration in the context of industrial consortia. One of the reasons that are often considered an obstacle to the adoption of this technology is given by the data privacy and confidentiality issues raised by exposing business transactions details and business relationships in a shared distributed ledger. In this paper we propose a solution based on a specific Hyperledger Fabric (HLF) feature, the Private Collections, which supports data privacy and confidentiality while keeping the advantages that the Blockchain distributed architecture brings.

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Published
2024-10-09
DOI
https://doi.org/10.1109/brains63024.2024.10732807
Primary Topic
Privacy-Preserving Technologies in Data
Type
article
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article

Preserving data privacy and confidentiality in a distributed ledger : a use case in logistics

Massimo Maresca, Pierpaolo Baglietto, A. Ferretto Parodi, Stefano Avola
Privacy-Preserving Technologies in Data
article

Preserving data privacy and confidentiality in a distributed ledger : a use case in logistics

Massimo Maresca, Pierpaolo Baglietto, A. Ferretto Parodi, Stefano Avola
article en

Abstract

Permissioned Blockchains have been proposed and assessed as a novel and promising approach for improving data sharing, trust, and integration in the context of industrial consortia. One of the reasons that are often considered an obstacle to the adoption of this technology is given by the data privacy and confidentiality issues raised by exposing business transactions details and business relationships in a shared distributed ledger. In this paper we propose a solution based on a specific Hyperledger Fabric (HLF) feature, the Private Collections, which supports data privacy and confidentiality while keeping the advantages that the Blockchain distributed architecture brings.

University of Genoa (IT)
Openalex Percentile: Top 82%
Privacy-Preserving Technologies in Data
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