A Survey on Privacy Properties for Data Publishing of Relational Data
Recent advances in telecommunications and database systems have allowed the scientific community to efficiently mine vast amounts of information worldwide and to extract new knowledge by discovering hidden patterns and correlations. Nevertheless, all this shared information can be used to invade the...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9032138/ |
| _version_ | 1811274153032941568 |
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| author | Athanasios Zigomitros Fran Casino Agusti Solanas Constantinos Patsakis |
| author_facet | Athanasios Zigomitros Fran Casino Agusti Solanas Constantinos Patsakis |
| author_sort | Athanasios Zigomitros |
| collection | DOAJ |
| description | Recent advances in telecommunications and database systems have allowed the scientific community to efficiently mine vast amounts of information worldwide and to extract new knowledge by discovering hidden patterns and correlations. Nevertheless, all this shared information can be used to invade the privacy of individuals through the use of fusion and mining techniques. Simply removing direct identifiers such as name, SSN, or phone number is not anymore sufficient to prevent against these practices. In numerous cases, other fields, like gender, date of birth and/or zipcode, can be used to re-identify individuals and to expose their sensitive details, e.g. their medical conditions, financial statuses and transactions, or even their private connections. The scope of this work is to provide an in-depth overview of the current state of the art in Privacy-Preserving Data Publishing (PPDP) for relational data. To counter information leakage, a number of data anonymisation methods have been proposed during the past few years, including <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-anonymity, <inline-formula> <tex-math notation="LaTeX">$\ell $ </tex-math></inline-formula>-diversity, <inline-formula> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula>-closeness, to name a few. In this study we analyse these methods providing concrete examples not only to explain how each of them works, but also to facilitate the reader to understand the different usage scenarios in which each of them can be applied. Furthermore, we detail several attacks along with their possible countermeasures, and we discuss open questions and future research directions. |
| first_indexed | 2024-04-12T23:12:35Z |
| format | Article |
| id | doaj.art-4f2373015982451b9d1ec3a18ad9e125 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T23:12:35Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-4f2373015982451b9d1ec3a18ad9e1252022-12-22T03:12:45ZengIEEEIEEE Access2169-35362020-01-018510715109910.1109/ACCESS.2020.29802359032138A Survey on Privacy Properties for Data Publishing of Relational DataAthanasios Zigomitros0https://orcid.org/0000-0003-1622-2815Fran Casino1https://orcid.org/0000-0003-4296-2876Agusti Solanas2https://orcid.org/0000-0002-4881-6215Constantinos Patsakis3https://orcid.org/0000-0002-4460-9331Department of Informatics, University of Piraeus, Pireas, GreeceDepartment of Informatics, University of Piraeus, Pireas, GreeceDepartment of Computer Engineering and Mathematics, Rovira i Virgili University, Tarragona, SpainDepartment of Informatics, University of Piraeus, Pireas, GreeceRecent advances in telecommunications and database systems have allowed the scientific community to efficiently mine vast amounts of information worldwide and to extract new knowledge by discovering hidden patterns and correlations. Nevertheless, all this shared information can be used to invade the privacy of individuals through the use of fusion and mining techniques. Simply removing direct identifiers such as name, SSN, or phone number is not anymore sufficient to prevent against these practices. In numerous cases, other fields, like gender, date of birth and/or zipcode, can be used to re-identify individuals and to expose their sensitive details, e.g. their medical conditions, financial statuses and transactions, or even their private connections. The scope of this work is to provide an in-depth overview of the current state of the art in Privacy-Preserving Data Publishing (PPDP) for relational data. To counter information leakage, a number of data anonymisation methods have been proposed during the past few years, including <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-anonymity, <inline-formula> <tex-math notation="LaTeX">$\ell $ </tex-math></inline-formula>-diversity, <inline-formula> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula>-closeness, to name a few. In this study we analyse these methods providing concrete examples not only to explain how each of them works, but also to facilitate the reader to understand the different usage scenarios in which each of them can be applied. Furthermore, we detail several attacks along with their possible countermeasures, and we discuss open questions and future research directions.https://ieeexplore.ieee.org/document/9032138/Data anonymizationprivacy preserving data publishingdata protectionk-anonymityprivacyreview |
| spellingShingle | Athanasios Zigomitros Fran Casino Agusti Solanas Constantinos Patsakis A Survey on Privacy Properties for Data Publishing of Relational Data IEEE Access Data anonymization privacy preserving data publishing data protection k-anonymity privacy review |
| title | A Survey on Privacy Properties for Data Publishing of Relational Data |
| title_full | A Survey on Privacy Properties for Data Publishing of Relational Data |
| title_fullStr | A Survey on Privacy Properties for Data Publishing of Relational Data |
| title_full_unstemmed | A Survey on Privacy Properties for Data Publishing of Relational Data |
| title_short | A Survey on Privacy Properties for Data Publishing of Relational Data |
| title_sort | survey on privacy properties for data publishing of relational data |
| topic | Data anonymization privacy preserving data publishing data protection k-anonymity privacy review |
| url | https://ieeexplore.ieee.org/document/9032138/ |
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