Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/5016
Title: Hiding sensitive predictive frequent itemsets
Authors: Yıldız, Barış
Ergenç, Belgin
Keywords: Frequent itemset mining
Privacy preserving data mining
Sensitive itemset hiding
Algorithms
Computer science
Issue Date: 2011
Publisher: International Association of Engineers
Source: Yıldız, B., and Ergenç, B. (2011). Hiding sensitive predictive frequent itemsets. Paper presented at the International MultiConference of Engineers and Computer Scientists 2011, IMECS 2011, Kowloon, Hong Kong, 16-18 March (pp. 339-345). Hong Kong: International Association of Engineers.
Abstract: In this work, we propose an itemset hiding algorithm with four versions that use different heuristics in selecting the item in itemset and the transaction for distortion. The main strengths of itemset hiding algorithm can be stated as i) it works without pre-mining so privacy breech caused by revealing frequent itemsets in advance is prevented and efficiency is increased, ii) base algorithm (Matrix-Apriori) works without candidate generation so efficiency is increased, iii) sanitized database and frequent itemsets of this database are given as outputs so no post-mining is required and iv) simple heuristics like the length of the pattern and the frequency of the item in the pattern are used for selecting the item for distortion. We compare versions of our itemset hiding algorithm by their side effects, runtimes and distortion on original database.
Description: International MultiConference of Engineers and Computer Scientists 2011, IMECS 2011; Kowloon; Hong Kong; 16 March 2011 through 18 March 2011
URI: http://hdl.handle.net/11147/5016
ISBN: 9789881821034
Appears in Collections:Computer Engineering / Bilgisayar Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Files in This Item:
File Description SizeFormat 
5016.pdfConference Paper869.34 kBAdobe PDFThumbnail
View/Open
Show full item record

CORE Recommender

Page view(s)

74
checked on Oct 3, 2022

Download(s)

24
checked on Oct 3, 2022

Google ScholarTM

Check

Altmetric


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