Privacy Preserving Machine Learning with Limited Information Leakage

Abstract

Machine learning is now playing important roles in daily lives, however, the privacy leakages are increasingly getting serious in the meantime. Current solutions to the privacy issues in machine learning, like differential privacy or homomorphic encryption either could only be applied to some specific scenarios or bring huge modification to the model construction, not to mention massive efficiency loss. In this paper, we consider addressing the privacy issue in machine learning from another perspective, without modification to models or severe efficiency loss. We proposed a straightforward privacy preserving machine learning scheme, training machine learning models directly over encrypted data. Ideally, this scheme could provide privacy protection to both training data and test data. We gave it a try by applying order preserving encryption (OPE) to the scheme. We discussed the possibility of using OPE to reveal the order information confidentially for model training. Several OPE algorithms were chosen to utilize the proposed method. Finally, comprehensive experiments were deployed on both synthetic and real datasets. The experiments on real datasets show that the learning performance of several well-known classifiers on before and after encryption changes slightly. The experiments on synthetic datasets show the classifier performance could be ranked according to fidelity and reliability.