Privacy-Preserving Boosting in the Local Setting

• URL: http://arxiv.org/abs/2002.02096v1
• Date: Thu, 6 Feb 2020 04:48:51 GMT
• Title: Privacy-Preserving Boosting in the Local Setting
• Authors: Sen Wang, J.Morris Chang
• Abstract summary: In machine learning, boosting is one of the most popular methods that designed to combine multiple base learners to a superior one. In the big data era, the data held by individual and entities, like personal images, browsing history and census information, are more likely to contain sensitive information. Local Differential Privacy is proposed as an effective privacy protection approach, which offers a strong guarantee to the data owners.
• Score: 17.375582978294105
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In machine learning, boosting is one of the most popular methods that designed to combine multiple base learners to a superior one. The well-known Boosted Decision Tree classifier, has been widely adopted in many areas. In the big data era, the data held by individual and entities, like personal images, browsing history and census information, are more likely to contain sensitive information. The privacy concern raises when such data leaves the hand of the owners and be further explored or mined. Such privacy issue demands that the machine learning algorithm should be privacy aware. Recently, Local Differential Privacy is proposed as an effective privacy protection approach, which offers a strong guarantee to the data owners, as the data is perturbed before any further usage, and the true values never leave the hands of the owners. Thus the machine learning algorithm with the private data instances is of great value and importance. In this paper, we are interested in developing the privacy-preserving boosting algorithm that a data user is allowed to build a classifier without knowing or deriving the exact value of each data samples. Our experiments demonstrate the effectiveness of the proposed boosting algorithm and the high utility of the learned classifiers.

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