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- W4313413556 abstract "Data quality is essential to pricing big data and deciding its trading profit in digital market. Traditional machine learning-based data quality assessment methods support the valuation of data assets. Nonetheless, these methods require data to be sent over and assessed at centralized cloud, which incurs unprecedented data transmission cost and may jeopardize data privacy. To address these issues, in this poster, we propose a privacy-preserving big data quality assessment scheme (p <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sup> QA) on the basis of blockchain and federated edge learning (FEEL). p <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sup> QA aims to notably reduce data transmission cost, accurately measure big data quality, and effectively prevent malicious parties from violating data privacy." @default.
- W4313413556 created "2023-01-06" @default.
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- W4313413556 date "2022-12-01" @default.
- W4313413556 modified "2023-09-28" @default.
- W4313413556 title "Poster: Blockchain-Enabled Federated Edge Learning for Big Data Quality Assessment" @default.
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- W4313413556 doi "https://doi.org/10.1109/sec54971.2022.00032" @default.
- W4313413556 hasPublicationYear "2022" @default.
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