Securely Training Decision Trees Efficiently

ePrint Report: Securely Training Decision Trees Efficiently

Divyanshu Bhardwaj, Sandhya Saravanan, Nishanth Chandran, Divya Gupta

Decision trees are an important class of supervised learning algorithms. When multiple entities contribute data to train a decision tree (e.g. for fraud detection in the financial sector), data privacy concerns necessitate the use of a privacy-enhancing technology such as secure multi-party computation (MPC) in order to secure the underlying training data. Prior state-of-the-art (Hamada et al.) construct an MPC protocol for decision …

algorithms class contribute data data privacy decision detection entities eprint report financial financial sector fraud fraud detection important privacy privacy concerns report sector technology train training trees