Privacy preserving in big data sets through multiple shuffle
Youyang Qu, Jiyang Xu, Shui Yu · Proceedings of the Australasian Computer Science Week Multiconference · 2016
Big data privacy-preserving has attracted increasing attention of researchers in recent years. But existing models are so complicated and time-consuming that they are not easy to implement. In this paper, we propose a more feasible and efficient model for big data sets privacy-preserving using shuffling multiple attributes(M-Shuffle) to achieve a tradeoff between data utility and privacy. Our strategy is firstly categorize all the records into some groups using K-means algorithm according to the sensitive attributes. Then we choose the columns to be shuffled using entropy. At last we introduce the random shuffle algorithm to our model to break the correlation among the columns of big data sets. Experiments on real-world datasets show that our framework achieves excellent data utility and efficiency while satisfying privacy-preserving.