Differentially Private XGBoost Algorithm for Traceability of Rice Varieties

Abstract

Privacy protection in agricultural traceability has received more and more attention. Most of the existing methods only protect the original data information from the perspective of cryptography and ignore the availability of the protected information. In fact, after data is processed by cryptography, blockchain, and other technologies, it cannot be directly used for machine learning model training. Therefore, differential privacy has great potential value for privacy protection in agricultural traceability, which can enable data to participate in classification tasks under privacy protection. In this paper, we propose an integrated algorithm for agricultural traceability called Differentially Private XGBoost (DP-XGB), which can protect the privacy of the original data during the training process and obtain high model utility under the condition of a small sample size. We inject Gaussian noise into the gradient operator and Hesse operator of the original XGBoost and give the calculation method of the resulting privacy budget. Experiments show that our method can effectively obtain differential privacy guarantees and achieves very high classification accuracy when the noise is small.

Keywords

• differential privacy
• variety traceability
• rice data safety
• machine learning