LSH-based private data protection for service quality with big range in distributed educational service recommendations

Service recommendation has become a promising way to extract useful or valuable information from big educational data collected by various sensors and distributed in different platforms. How to protect the private user data in each cluster during recommendation processes is an interesting but challenging problem in the educational domain. A locality-sensitive hashing (LSH) technique has recently been employed to achieve the abovementioned privacy-preservation goal. However, traditional LSH-based recommendation approaches often suffer from low accuracy when the service quality data recruited in recommendations vary in a big range. Considering this drawback, we modify the traditional LSH technique in this paper to make it applicable to the service quality data with a big range, so as to pursue privacy-preserving and an accurate recommended list. Finally, a wide range of experiments are conducted based on the distributed dataset, i.e., WS-DREAM. Experiment results show that our approach can protect the private data in education (e.g., student information in universities) and performs better than other state-of-the-art ones in terms of accuracy and efficiency.

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