@Article{cmc.2020.010987,
AUTHOR = {Haijiang Liu, Lianwei Cui, Xuebin Ma, Celimuge Wu},
TITLE = {Frequent Itemset Mining of User’s Multi-Attribute under Local  Differential Privacy},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {1},
PAGES = {369--385},
URL = {http://www.techscience.com/cmc/v65n1/39571},
ISSN = {1546-2226},
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 multi-attribute. The main 
technique has bitmap encoding for converting the user’s original data into a binary string. 
It also includes how to choose the best perturbation algorithm for varying user attributes, 
and uses the frequent pattern tree (FP-tree) algorithm to mine frequent itemsets. Finally, 
we incorporate the threshold random response (TRR) algorithm in the framework and 
compare it with the existing algorithms, and demonstrate that the TRR algorithm has 
higher accuracy for mining frequent itemsets.},
DOI = {10.32604/cmc.2020.010987}
}