Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/79732
Title: Frequent itemsets mining with differential privacy over large-scale data
Authors: Xiong, XY
Chen, F 
Huang, PZ
Tian, MM
Hu, XF
Chen, BD
Qin, J 
Keywords: Frequent itemsets mining
Differential privacy
Sampling
Transaction truncation
String matching
Issue Date: 2018
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE access, 2018, v. 6, p. 28877-28889 How to cite?
Journal: IEEE access 
Abstract: Frequent itemsets mining with differential privacy refers to the problem of mining all frequent itemsets whose supports are above a given threshold in a given transactional dataset, with the constraint that the mined results should not break the privacy of any single transaction. Current solutions for this problem cannot well balance efficiency, privacy, and data utility over large-scale data. Toward this end, we propose an efficient, differential private frequent itemsets mining algorithm over large-scale data. Based on the ideas of sampling and transaction truncation using length constraints, our algorithm reduces the computation intensity, reduces mining sensitivity, and thus improves data utility given a fixed privacy budget. Experimental results show that our algorithm achieves better performance than prior approaches on multiple datasets.
URI: http://hdl.handle.net/10397/79732
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2018.2839752
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

1
Citations as of Feb 15, 2019

WEB OF SCIENCETM
Citations

1
Citations as of Feb 16, 2019

Page view(s)

6
Citations as of Feb 18, 2019

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.