Reducing Side Effects of Hiding Sensitive Itemsets in Privacy Preserving Data Mining
Data mining is traditionally adopted to retrieve and analyze knowledge from large amounts of data. Private or confidential data may be sanitized or suppressed before it is shared or published in public. Privacy preserving data mining (PPDM) has thus become an important issue in recent years. The mos...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
|
| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/235837 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832567664624533504 |
|---|---|
| author | Chun-Wei Lin Tzung-Pei Hong Hung-Chuan Hsu |
| author_facet | Chun-Wei Lin Tzung-Pei Hong Hung-Chuan Hsu |
| author_sort | Chun-Wei Lin |
| collection | DOAJ |
| description | Data mining is traditionally adopted to retrieve and analyze knowledge from large amounts of data. Private or confidential data may be sanitized or suppressed before it is shared or published in public. Privacy preserving data mining (PPDM) has thus become an important issue in recent years. The most general way of PPDM is to sanitize the database to hide the sensitive information. In this paper, a novel hiding-missing-artificial utility (HMAU) algorithm is proposed to hide sensitive itemsets through transaction deletion. The transaction with the maximal ratio of sensitive to nonsensitive one is thus selected to be entirely deleted. Three side effects of hiding failures, missing itemsets, and artificial itemsets are considered to evaluate whether the transactions are required to be deleted for hiding sensitive itemsets. Three weights are also assigned as the importance to three factors, which can be set according to the requirement of users. Experiments are then conducted to show the performance of the proposed algorithm in execution time, number of deleted transactions, and number of side effects. |
| format | Article |
| id | doaj-art-989ed1a0807a44a789ba2198b3337bad |
| 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-989ed1a0807a44a789ba2198b3337bad2025-02-03T01:00:51ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/235837235837Reducing Side Effects of Hiding Sensitive Itemsets in Privacy Preserving Data MiningChun-Wei Lin0Tzung-Pei Hong1Hung-Chuan Hsu2Innovative Information Industry Research Center (IIIRC), School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen 518055, ChinaDepartment of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung 811, TaiwanDepartment of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung 811, TaiwanData mining is traditionally adopted to retrieve and analyze knowledge from large amounts of data. Private or confidential data may be sanitized or suppressed before it is shared or published in public. Privacy preserving data mining (PPDM) has thus become an important issue in recent years. The most general way of PPDM is to sanitize the database to hide the sensitive information. In this paper, a novel hiding-missing-artificial utility (HMAU) algorithm is proposed to hide sensitive itemsets through transaction deletion. The transaction with the maximal ratio of sensitive to nonsensitive one is thus selected to be entirely deleted. Three side effects of hiding failures, missing itemsets, and artificial itemsets are considered to evaluate whether the transactions are required to be deleted for hiding sensitive itemsets. Three weights are also assigned as the importance to three factors, which can be set according to the requirement of users. Experiments are then conducted to show the performance of the proposed algorithm in execution time, number of deleted transactions, and number of side effects.http://dx.doi.org/10.1155/2014/235837 |
| spellingShingle | Chun-Wei Lin Tzung-Pei Hong Hung-Chuan Hsu Reducing Side Effects of Hiding Sensitive Itemsets in Privacy Preserving Data Mining The Scientific World Journal |
| title | Reducing Side Effects of Hiding Sensitive Itemsets in Privacy Preserving Data Mining |
| title_full | Reducing Side Effects of Hiding Sensitive Itemsets in Privacy Preserving Data Mining |
| title_fullStr | Reducing Side Effects of Hiding Sensitive Itemsets in Privacy Preserving Data Mining |
| title_full_unstemmed | Reducing Side Effects of Hiding Sensitive Itemsets in Privacy Preserving Data Mining |
| title_short | Reducing Side Effects of Hiding Sensitive Itemsets in Privacy Preserving Data Mining |
| title_sort | reducing side effects of hiding sensitive itemsets in privacy preserving data mining |
| url | http://dx.doi.org/10.1155/2014/235837 |
| work_keys_str_mv | AT chunweilin reducingsideeffectsofhidingsensitiveitemsetsinprivacypreservingdatamining AT tzungpeihong reducingsideeffectsofhidingsensitiveitemsetsinprivacypreservingdatamining AT hungchuanhsu reducingsideeffectsofhidingsensitiveitemsetsinprivacypreservingdatamining |