Hadamard Encoding Based Frequent Itemset Mining under Local Differential Privacy

• Published in: Journal of computer science and technology Vol. 38; no. 6; pp. 1403 - 1422
• Main Authors: Zhao, Dan, Zhao, Su-Yun, Chen, Hong, Liu, Rui-Xuan, Li, Cui-Ping, Zhang, Xiao-Ying
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.12.2023; Springer Nature B.V
• Subjects: Artificial Intelligence; Coding; Computer Science; Data mining; Data Structures and Information Theory; Information Systems Applications (incl.Internet); Mining; Perturbation; Privacy; Regular Paper; Sampling; Software Engineering; Theory of Computation; local differential privacy; frequency oracle; frequent itemset mining

Local differential privacy (LDP) approaches to collecting sensitive information for frequent itemset mining (FIM) can reliably guarantee privacy. Most current approaches to FIM under LDP add “padding and sampling” steps to obtain frequent itemsets and their frequencies because each user transaction...