Multi-level high utility-itemset hiding.
Privacy is as a critical issue in the age of data. Organizations and corporations who publicly share their data always have a major concern that their sensitive information may be leaked or extracted by rivals or attackers using data miners. High-utility itemset mining (HUIM) is an extension to freq...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0317427 |
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| _version_ | 1823864054126477312 |
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| author | Loan T T Nguyen Hoa Duong An Mai Bay Vo |
| author_facet | Loan T T Nguyen Hoa Duong An Mai Bay Vo |
| author_sort | Loan T T Nguyen |
| collection | DOAJ |
| description | Privacy is as a critical issue in the age of data. Organizations and corporations who publicly share their data always have a major concern that their sensitive information may be leaked or extracted by rivals or attackers using data miners. High-utility itemset mining (HUIM) is an extension to frequent itemset mining (FIM) which deals with business data in the form of transaction databases, data that is also in danger of being stolen. To deal with this, a number of privacy-preserving data mining (PPDM) techniques have been introduced. An important topic in PPDM in the recent years is privacy-preserving utility mining (PPUM). The goal of PPUM is to protect the sensitive information, such as sensitive high-utility itemsets, in transaction databases, and make them undiscoverable for data mining techniques. However, available PPUM methods do not consider the generalization of items in databases (categories, classes, groups, etc.). These algorithms only consider the items at a specialized level, leaving the item combinations at a higher level vulnerable to attacks. The insights gained from higher abstraction levels are somewhat more valuable than those from lower levels since they contain the outlines of the data. To address this issue, this work suggests two PPUM algorithms, namely MLHProtector and FMLHProtector, to operate at all abstraction levels in a transaction database to protect them from data mining algorithms. Empirical experiments showed that both algorithms successfully protect the itemsets from being compromised by attackers. |
| format | Article |
| id | doaj-art-070645eff21048199dd092dc256c7eab |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-070645eff21048199dd092dc256c7eab2025-02-09T05:30:40ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01202e031742710.1371/journal.pone.0317427Multi-level high utility-itemset hiding.Loan T T NguyenHoa DuongAn MaiBay VoPrivacy is as a critical issue in the age of data. Organizations and corporations who publicly share their data always have a major concern that their sensitive information may be leaked or extracted by rivals or attackers using data miners. High-utility itemset mining (HUIM) is an extension to frequent itemset mining (FIM) which deals with business data in the form of transaction databases, data that is also in danger of being stolen. To deal with this, a number of privacy-preserving data mining (PPDM) techniques have been introduced. An important topic in PPDM in the recent years is privacy-preserving utility mining (PPUM). The goal of PPUM is to protect the sensitive information, such as sensitive high-utility itemsets, in transaction databases, and make them undiscoverable for data mining techniques. However, available PPUM methods do not consider the generalization of items in databases (categories, classes, groups, etc.). These algorithms only consider the items at a specialized level, leaving the item combinations at a higher level vulnerable to attacks. The insights gained from higher abstraction levels are somewhat more valuable than those from lower levels since they contain the outlines of the data. To address this issue, this work suggests two PPUM algorithms, namely MLHProtector and FMLHProtector, to operate at all abstraction levels in a transaction database to protect them from data mining algorithms. Empirical experiments showed that both algorithms successfully protect the itemsets from being compromised by attackers.https://doi.org/10.1371/journal.pone.0317427 |
| spellingShingle | Loan T T Nguyen Hoa Duong An Mai Bay Vo Multi-level high utility-itemset hiding. PLoS ONE |
| title | Multi-level high utility-itemset hiding. |
| title_full | Multi-level high utility-itemset hiding. |
| title_fullStr | Multi-level high utility-itemset hiding. |
| title_full_unstemmed | Multi-level high utility-itemset hiding. |
| title_short | Multi-level high utility-itemset hiding. |
| title_sort | multi level high utility itemset hiding |
| url | https://doi.org/10.1371/journal.pone.0317427 |
| work_keys_str_mv | AT loanttnguyen multilevelhighutilityitemsethiding AT hoaduong multilevelhighutilityitemsethiding AT anmai multilevelhighutilityitemsethiding AT bayvo multilevelhighutilityitemsethiding |