A Framework for a Blockchain-Based Decentralized Data Marketplace

Aljohani, Meshari; Mukkamala, Ravi; Olariu, Stephan

doi:10.1007/978-3-031-27041-3_5

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…We start describing our framework by (1) detailing the main entities involved in the marketplace and how they interact. Then, (2) we introduce the adversary model and the security definitions relevant to our proposed system. Finally, (3) we outline each of the phases of our decentralized information market protocol.…”

Section: The Frameworkmentioning

confidence: 99%

“…Personal information is routinely collected by companies and government agencies without giving individuals much control over how their data is used, or the benefits they receive in return for their data. The tension between the desire to promote an economy based on free-flowing data on one hand, and the need to protect privacy on the other hand -as reflected in new regulations such as the GDPR 1 , the CCPA 2 , and the AI Bill of Rights 3 -can be eased by Privacy-Enhancing Technologies (PETs), as we do in this work. We propose a decentralized information marketplace where data held by data providers -such as individual users -can be made available for computation to data consumers -such as government agencies, research institutes, or companies who want to derive actionable insights or train machine learning models with the data -while (1) protecting input privacy, (2) protecting output privacy, and (3) compensating data providers for providing their sensitive information as input for secure computations.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Decentralized Information Marketplace Preserving Input and Output Privacy

Golob,

Pentyala,

Dowsley

et al. 2023

Proceedings of the Second ACM Data Economy Workshop

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Data-driven applications are engines of economic growth and essential for progress in many domains. The data involved is often of a personal nature. We propose a decentralized information marketplace where data held by data providers, such as individual users can be made available for computation to data consumers, such as government agencies, research institutes, or companies who want to derive actionable insights or train machine learning models with the data while (1) protecting input privacy, (2) protecting output privacy, and (3) compensating data providers for making their sensitive information available for secure computation. We enable this privacy-preserving data exchange through a novel and carefully designed combination of a blockchain that supports smart contracts and two privacy-enhancing technologies: (1) secure multi-party computations, and (2) robust differential privacy guarantees. CCS CONCEPTS• Security and privacy → Economics of security and privacy; Privacy protections; Management and querying of encrypted data; Privacy-preserving protocols; • Theory of computation → Cryptographic protocols.

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Section: The Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%