Collaborative Regression Analysis Algorithm for Multi Organizational Coupling Feature to Ensure Privacy: LARS Based on Federal Learning
Benqiang Mao, Zhaogong Zhang · 2019
With the development of big data, emphasis on data privacy and security has become a global trend. To address data privacy and security issues, Google has proposed a federal learning framework designed to make effective and accurate cross-organizational use of data within the industry. Suppose two organizations, A and B, each maintaining a private record of different sets of features for a common entity. If there is no coupling between the features, the processing is relatively simple. Assuming strong coupling between features, how to deal with it is relatively complicated, both to ensure privacy and to obtain coupling relationships, such as inner product calculation results. We designed and implemented a framework that guarantees privacy and efficient coupling. The experimental results show that the scheme is feasible, the data privacy is guaranteed, the feature coupling between different organizations is calculated, the result is accurate, and the execution time is acceptable.