Privacy-preserving and high-accurate outsourced disease predictor on random forest

Training data distributed across multiple different institutions is ubiquitous in disease prediction applications. Data collection may involve multiple data sources who are willing to contribute their datasets to train a more precise classifier with a larger training set. Nevertheless, integrating multiple-source datasets will leak sensitive information to untrusted data sources. Hence, it is imperative to protect multiple-source data privacy during the predictor construction process. Besides, since disease diagnosis is strongly associated with health and life, it is vital to guarantee prediction accuracy. In this paper, we propose a privacy-preserving and high-accurate outsourced disease predictor on random forest, called PHPR. PHPR system can perform secure training with medical information which belongs to different data owners, and make accurate prediction. Besides, the original data and computed results in the rational field can be securely processed and stored in cloud without privacy leakage. Specifically, we first design privacy-preserving computation protocols over rational numbers to guarantee computation accuracy and handle outsourced operations on-the-fly. Then, we demonstrate that PHPR system achieves secure disease predictor. Finally, the experimental results using real-world datasets demonstrate that PHPR system not only provides secure disease predictor over ciphertexts, but also maintains the prediction accuracy as the original classifier. (C) 2019 Elsevier Inc. All rights reserved.