Poster: End-to-End Privacy-Preserving Vertical Federated Learning using Private Cross-Organizational Data Collaboration

Keiichi Ochiai, Masayuki Terada

Abstract: As data utilization in organizations is advancing in various fields, insights that data brings will be more diverse when it is sourced through collaboration across different organizations. Federated learning, a machine learning method with distributed data across organizations, with local differential privacy protects privacy by sharing only the model parameters and the information necessary for model update, without having to share the data each organization holds. However, there is a problem with local differential privacy, where the amount of noise increases, leading to the degradation in model accuracy. In this paper, we propose a method of reducing the impact of noise compared to conventional federated learning by leveraging private cross-organizational data collaboration, called Private Cross-aggregation Technology (PCT). PCT combines Private Set Intersection Cardinality, Trusted Execution Environment and Differential Privacy, and outputs a cross-tabulation table that is private from input to output. Our method consists of two steps: (1) creating a private cross-tabulation table using PCT, and (2) training a ML using the private cross-tabulation table. The experiment results showed that (1) the classification accuracy of the proposed method was higher than that of the baseline method in situations where the privacy budget is limited, and (2) the computation time of the proposed method was shorter than that of the baseline method.