Differentially Private Frequent Itemset Mining via Transaction Splitting

@article{Su2015DifferentiallyPF,
  title={Differentially Private Frequent Itemset Mining via Transaction Splitting},
  author={Sen Su and Shengzhi Xu and Xiang Cheng and Zhengyi Li and Fangchun Yang},
  journal={2016 IEEE 32nd International Conference on Data Engineering (ICDE)},
  year={2015},
  pages={1564-1565}
}
Recently, there has been a growing interest in designing differentially private data mining algorithms. Frequent itemset mining (FIM) is one of the most fundamental problems in data mining. In this paper, we explore the possibility of designing a differentially private FIM algorithm which can not only achieve high data utility and a high degree of privacy, but also offer high time efficiency. To this end, we propose a differentially private FIM algorithm based on the FP-growth algorithm, which… CONTINUE READING
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