隐私计算场景下数据质量治理探索与实践

doi:10.11959/j.issn.2096-0271.2022073

Abstract

Abstract:

Privacy computing is a new data processing technology, which can realize the transformation and circulation of a data value on the premise of protecting data privacy and security.However, the invisible feature of data in private computing scenarios poses a great challenge to traditional data quality management.There is still a lack of perfect solutions.To solve the above problems in the industry, a data quality governance method and process suitable for privacy computing scenarios were proposed.A local and multi-party data quality evaluation system was constructed, which could take into account the data quality governance of the local domain and the federal domain.At the same time, a data contribution measurement method was proposed to explore the long-term incentive mechanism of privacy computing, improve the data quality of privacy computing, and improve the accuracy of computing results.

Key words: privacy computing, federated learning, data quality governance, data contribution

CLC Number:

D922.16，TP18

Yan ZHANG, Yifan YANG, Ren YI, Shengmei LUO, Jianfei TANG, Zhengxun XIA. Exploration and practice of data quality governance in privacy computing scenarios[J]. Big Data Research, 2022, 8(5): 55-73.

Figures/Tables 10

References 46

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