Personalized Privacy-Preserving Frequent Itemset Mining Using Randomized Response
Frequent itemset mining is the important first step of association rule mining, which discovers interesting patterns from the massive data. There are increasing concerns about the privacy problem in the frequent itemset mining. Some works have been proposed to handle this kind of problem. In this pa...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/686151 |
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| _version_ | 1832564003459563520 |
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| author | Chongjing Sun Yan Fu Junlin Zhou Hui Gao |
| author_facet | Chongjing Sun Yan Fu Junlin Zhou Hui Gao |
| author_sort | Chongjing Sun |
| collection | DOAJ |
| description | Frequent itemset mining is the important first step of association rule mining, which discovers interesting patterns from the massive data. There are increasing concerns about the privacy problem in the frequent itemset mining. Some works have been proposed to handle this kind of problem. In this paper, we introduce a personalized privacy problem, in which different attributes may need different privacy levels protection. To solve this problem, we give a personalized privacy-preserving method by using the randomized response technique. By providing different privacy levels for different attributes, this method can get a higher accuracy on frequent itemset mining than the traditional method providing the same privacy level. Finally, our experimental results show that our method can have better results on the frequent itemset mining while preserving personalized privacy. |
| format | Article |
| id | doaj-art-a6d1992e7eb74c68992ae99d57b7b30b |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-a6d1992e7eb74c68992ae99d57b7b30b2025-02-03T01:12:02ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/686151686151Personalized Privacy-Preserving Frequent Itemset Mining Using Randomized ResponseChongjing Sun0Yan Fu1Junlin Zhou2Hui Gao3Web Science Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaWeb Science Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaWeb Science Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaWeb Science Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaFrequent itemset mining is the important first step of association rule mining, which discovers interesting patterns from the massive data. There are increasing concerns about the privacy problem in the frequent itemset mining. Some works have been proposed to handle this kind of problem. In this paper, we introduce a personalized privacy problem, in which different attributes may need different privacy levels protection. To solve this problem, we give a personalized privacy-preserving method by using the randomized response technique. By providing different privacy levels for different attributes, this method can get a higher accuracy on frequent itemset mining than the traditional method providing the same privacy level. Finally, our experimental results show that our method can have better results on the frequent itemset mining while preserving personalized privacy.http://dx.doi.org/10.1155/2014/686151 |
| spellingShingle | Chongjing Sun Yan Fu Junlin Zhou Hui Gao Personalized Privacy-Preserving Frequent Itemset Mining Using Randomized Response The Scientific World Journal |
| title | Personalized Privacy-Preserving Frequent Itemset Mining Using Randomized Response |
| title_full | Personalized Privacy-Preserving Frequent Itemset Mining Using Randomized Response |
| title_fullStr | Personalized Privacy-Preserving Frequent Itemset Mining Using Randomized Response |
| title_full_unstemmed | Personalized Privacy-Preserving Frequent Itemset Mining Using Randomized Response |
| title_short | Personalized Privacy-Preserving Frequent Itemset Mining Using Randomized Response |
| title_sort | personalized privacy preserving frequent itemset mining using randomized response |
| url | http://dx.doi.org/10.1155/2014/686151 |
| work_keys_str_mv | AT chongjingsun personalizedprivacypreservingfrequentitemsetminingusingrandomizedresponse AT yanfu personalizedprivacypreservingfrequentitemsetminingusingrandomizedresponse AT junlinzhou personalizedprivacypreservingfrequentitemsetminingusingrandomizedresponse AT huigao personalizedprivacypreservingfrequentitemsetminingusingrandomizedresponse |