Abstract
A wide range of data mining applications benefit from the low latency offered by edge computing. However, edge computing suffers from limited computing resources, which inhibits the applications of the computationally expensive data mining methods. In the edge-cloud environment, usually, the participants turn to collaboratively train machine-learning models that yield more accurate prediction results. However, data owners may not be willing to sharing the own data for the privacy concerns. To handle such disparate goals, we focus on tree-based distributed data mining scheme with differential privacy, which is computationally friendly. The basic idea of our approach is based on a distributed ensemble strategy. Each participant builds an elegant decision model based on their own data, which has a good tradeoff between the computation and the accuracy of the data distribution, and shares it with other participants after being injected with the elaborate noise. Then the useful knowledge transferred from the decision models is acquired by other participants in an adaptive ensemble strategy. Both the theoretical analysis and the experiments show that our scheme provides an efficient data mining manner that can achieve a good prediction accuracy while providing rigorous privacy guarantee over the distributed data.
Highlights
During the past few years, with the advent of the edge computing, numerous smart devices have been popularized and subsequently massive data has been produced [1]
A wide range of data mining applications benefit from the low latency offered by edge computing
(2) We propose an adaptive ensemble strategy for the construction process, which allows the participants to improve the prediction accuracy of the basic learner by combining the models with similar data distribution without access to these private data
Summary
During the past few years, with the advent of the edge computing, numerous smart devices have been popularized and subsequently massive data has been produced [1]. Data mining has been serving as an epoch-making technique to extract the hidden information and valuable knowledge automatically and intelligently. A wide range of data mining applications in edge computing have been deployed to benefit our daily lives, e.g. smart healthcare [2], smart homing [3], and intelligent transportation [4, 5], etc. A wide range of data mining applications benefit from the low latency offered by edge computing. Existing researches on privacy-preserving distributed data mining mainly focus on cryptography [7,8,9].
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