Privacy-Preserving Boosting in the Local Setting

• Published in: IEEE transactions on information forensics and security Vol. 16; pp. 4451 - 4465
• Main Authors: Wang, Sen, Chang, J. Morris
• Format: Journal Article
• Language: English
• Published: New York IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Boosting; Classifiers; Data mining; Data models; Decision trees; Differential privacy; Ensemble learning; local differential privacy; Machine learning; Machine learning algorithms; Object recognition; Pattern recognition; Prediction algorithms; Privacy

In machine learning, boosting is one of the most popular methods that is designed to combine multiple base learners into a superior one. The well-known Boosted Decision Tree classifier has been widely adopted in data mining and pattern recognition. With the emerging challenge in privacy, the persona...