DeepLinQ: Distributed Multi-Layer Ledgers for Privacy-Preserving Data Sharing
| Autor: | Emily J. Chang, Pin-Wei Liao, Chun-Ting Liu, Shih-Wei Liao, Lin Wei-Chen, Wei-Kang Fu, Edward Y. Chang, Chung-Huan Mei |
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| Rok vydání: | 2018 |
| Předmět: |
Flexibility (engineering)
Blockchain business.industry Computer science Distributed computing Big data 020207 software engineering Access control 02 engineering and technology 010501 environmental sciences 01 natural sciences Data sharing Accountability Scalability 0202 electrical engineering electronic engineering information engineering Key (cryptography) business 0105 earth and related environmental sciences |
| Zdroj: | AIVR |
| DOI: | 10.1109/aivr.2018.00037 |
| Popis: | This paper presents requirements to DeepLinQ and its architecture. DeepLinQ proposes a multi-layer blockchain architecture to improve flexibility, accountability, and scalability through on-demand queries, proxy appointment, subgroup signatures, granular access control, and smart contracts in order to support privacy-preserving distributed data sharing. In this data-driven AI era where big data is the prerequisite for training an effective deep learning model, DeepLinQ provides a trusted infrastructure to enable training data collection in a privacy-preserved way. This paper uses healthcare data sharing as an application example to illustrate key properties and design of DeepLinQ. |
| Databáze: | OpenAIRE |
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