Random Forest Algorithm Based on Linear Privacy Budget Allocation

Abstract

In the era of big data with exponential growth in data volume, how to reduce data security issues such as data leakage caused by machine learning is a hot area of recent research. The existing privacy budget allocation strategies are usually only suitable for data applications in specific spaces and cannot meet users' personalized needs for privacy budget allocation. Therefore, a linear privacy budget allocation strategy is proposed. The strategy assigns each layer a linearly increasing privacy budget from the root of the decision tree to the bottom by adjusting the coefficient or constant term. Combining this strategy with the random forest algorithm, a random forest algorithm based on linear privacy budget allocation (DiffPRF_linear) is formed. Experimental results show that the proposed algorithm can realize uniform, arithmetic, and geometric privacy budget allocation policy effects and can also achieve better classification effects than the former, which not only meets the needs of users to protect private data personalized but also maintains high classification accuracy.

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