https://scholars.lib.ntu.edu.tw/handle/123456789/489697
Title: | DeepLinQ: Distributed multi-layer ledgers for privacy-preserving data sharing | Authors: | Chang, E.Y. SHIH-WEI LIAO Liu, C.-T. Lin, W.-C. Liao, P.-W. Fu, W.-K. Mei, C.-H. |
Keywords: | Blockchain; Consensus protocol; Ledger system; Privacy preserving; Smart contract | Issue Date: | 2019 | Start page/Pages: | 173-178 | Source: | Proceedings - 2018 IEEE International Conference on Artificial Intelligence and Virtual Reality, AIVR 2018 | Abstract: | 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. © 2018 IEEE. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/489697 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062187518&doi=10.1109%2fAIVR.2018.00037&partnerID=40&md5=f0dac7b3355e36d97a0347dfd4b3cf3b |
DOI: | 10.1109/AIVR.2018.00037 | SDG/Keyword: | Access control; Artificial intelligence; Blockchain; Deep learning; Virtual reality; Application examples; Consensus protocols; Distributed data; ITS architecture; Learning models; Ledger system; Privacy preserving; Training data; Data privacy |
Appears in Collections: | 資訊工程學系 |
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