An Effective Data Privacy Protection Algorithm Based on Differential Privacy in Edge Computing

Abstract

With the rapid development of information science and the Internet of Things (IoT), people have an unprecedented ability to collect and share data, especially the various sensors as the entrance to data collection. At the same time, edge computing has begun to grasp the public's attention because of the difficult challenges of massive equipment access and massive data. Although such large amount of data provides a huge opportunity for information discovery, the privacy leakage has also been concerned. When the data publisher publishes various statistics, the attacker can obtain the statistical rules in the data by simply using the query function without contacting the user or the data publisher. Therefore, how to protect the data privacy of statistical information has become the focus of attention. In this paper, we proposed a partitioned histogram data publishing algorithm based on wavelet transform. Firstly, a partitioning algorithm based on greedy algorithm is used to obtain a better partition structure. Then, we use wavelet transform to add noise. Finally, for the authenticity and usability of histogram, we get the reductive original histogram structure. On the one hand, our algorithm can reduce the complexity of wavelet tree constructed by wavelet transform. On the other hand, the query noise changes from linear growth to multiple logarithm growth. So the accuracy of histogram counting query is improved. Experiments show that our algorithm has the significant improvement in data availability.

Keywords

• Internet of Things
• edge computing
• wavelet transforms
• differential privacy