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.
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
·
System : Pentium Dual Core.
·
Hard Disk : 120 GB.
·
Monitor : 15’’ LED
·
Input Devices : Keyboard, Mouse
·
Ram : 1 GB
SOFTWARE REQUIREMENTS:
·
Operating system : Windows 7.
·
Coding Language : JAVA/J2EE
·
Tool : Netbeans 7.2.1
·
Database : MYSQL
REFERENCE:
XINYU XIONG1, FEI
CHEN 1,2, PEIZHI HUANG1, MIAOMIAO TIAN3, XIAOFANG HU4, BADONG CHEN 5, AND JING
QIN, “Frequent Itemsets Mining With Differential Privacy Over Large-Scale
Data”, IEEE Access, 2018.