Haijiang Liu1, Lianwei Cui2, Xuebin Ma1, *, Celimuge Wu3
CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 369-385, 2020, DOI:10.32604/cmc.2020.010987
Abstract Frequent itemset mining is an essential problem in data mining and plays a key
role in many data mining applications. However, users’ personal privacy will be leaked in
the mining process. In recent years, application of local differential privacy protection
models to mine frequent itemsets is a relatively reliable and secure protection method.
Local differential privacy means that users first perturb the original data and then send
these data to the aggregator, preventing the aggregator from revealing the user’s private
information. We propose a novel framework that implements frequent itemset mining
under local differential privacy and is applicable to user’s… More >
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