Privacy-preserving feature selection allows identifying more important features while ensuring data privacy, thus enhancing data quality. Secure multiparty computation (MPC) is a cryptographic method that allows effective data processing without a trusted third party. However, most MPC-based feature selection schemes overlook the correlation between features and perform poorly for model training when handling datasets containing both numerical and categorical attributes.
To solve the problem, a research team led by Lu Zhou published their new research on 15 March 2026 in Frontiers of Computer Science co-published by Higher Education Press and Springer Nature.
The team proposed a feature selection scheme, MPC-Relief, to select the relevant features while preserving privacy. To achieve safety under MPC, they transform all complex computational steps from data-dependent to data-oblivious with faithful implementations.
In the research, they construct a nonlinear function based on MPC, called MN-Ramp, to solve the problem of distance calculation. They apply this function to the Relief algorithm to handle distance calculation when dealing with numerical and categorical features. They construct a bidirectional vector and adopt the mapping method to estimate the near instances, which avoids conditional judgments. They implement MPC-Relief and evaluate it on two computational environments and several datasets. The experimental results show that the scheme can achieve effective feature selection. Future work can optimize the performance of the time-consuming modules and construct a robust privacy-preserving feature selection scheme.
Frontiers of Computer Science
Experimental study
Not applicable
Enhancing data quality with effective feature selection and privacy protection
15-Mar-2026