DeepLinQ: Distributed multi-layer ledgers for privacy-preserving data sharing
Journal
Proceedings - 2018 IEEE International Conference on Artificial Intelligence and Virtual Reality, AIVR 2018
Pages
173-178
Date Issued
2019
Author(s)
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.
Subjects
Blockchain; Consensus protocol; Ledger system; Privacy preserving; Smart contract
SDGs
Other Subjects
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
Type
conference paper