Exploration and practice of data quality governance in privacy computing scenarios

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.

Keywords

• privacy computing;federated learning;data quality governance;data contribution