Open AccessPrivacy Protection of Frequent Sequences based on Uniform Adaptive Local Margin
Author(s) -
Zhihan Wang,
Yiwei Qiu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1852/4/042001
Subject(s) - differential privacy , margin (machine learning) , tree traversal , computer science , sensitivity (control systems) , data mining , dimension (graph theory) , set (abstract data type) , identification (biology) , algorithm , mathematics , machine learning , engineering , botany , electronic engineering , biology , pure mathematics , programming language
In order to enhance the privacy protection of frequent sequences, and improve its mining utility and reduce the impact of data dimension, this paper proposes a consistent adaptive margin for marginal release under local difference privacy, which has the characteristics of improving the effectiveness and efficiency. The algorithm mainly uses the consistent adaptive local margin strategy to index, finds frequent sequences, perturbs transaction data through random response mechanism, and finds all the sensitivities that satisfy local differential privacy. For the purpose of determining the running time of mining frequent sequences, a reasonable local sensitivity traversal data set before and after the interference is selected. According to the combination property of differential privacy, it is proved theoretically that the algorithm satisfies local differential privacy, and the effectiveness of the algorithm is verified by experiments. The experimental results show that the proposed algorithm can protect the local differential privacy of frequent sequences safely and efficiently, and ensure the accuracy of frequent sequences.