Logical foundations of privacy-preserving publishing of linked data
<p>The widespread adoption of Linked Data has been driven by the increasing demand for information exchange between organisations, as well as by data publishing regulations in domains such as health care and governance. In this setting, sensitive information is at risk of disclosure since publ...
| Main Authors: | , |
|---|---|
| Format: | Conference item |
| Published: |
Association for the Advancement of Artificial Intelligence
2015
|
| _version_ | 1826272208396222464 |
|---|---|
| author | Cuenca Grau, B Kostylev, E |
| author_facet | Cuenca Grau, B Kostylev, E |
| author_sort | Cuenca Grau, B |
| collection | OXFORD |
| description | <p>The widespread adoption of Linked Data has been driven by the increasing demand for information exchange between organisations, as well as by data publishing regulations in domains such as health care and governance. In this setting, sensitive information is at risk of disclosure since published data can be linked with arbitrary external data sources</p> <p>In this paper we lay the foundations of privacy-preserving data publishing (PPDP) in the context of Linked Data. We consider anonymisations of RDF graphs (and, more generally, relational datasets with labelled nulls) and define notions of safe and optimal anonymisations. Safety ensures that the anonymised data can be published with provable protection guarantees against linking attacks, whereas optimality ensures that it preserves as much information from the original data as possible, while satisfying the safety requirement. We establish the complexity of the underpinning decision problems both under open-world semantics inherent to RDF and a closed-world semantics, where we assume that an attacker has complete knowledge over some part of the original data.</p> |
| first_indexed | 2024-03-06T22:08:54Z |
| format | Conference item |
| id | oxford-uuid:51203886-45e9-430c-b95c-c0ea6d238656 |
| institution | University of Oxford |
| last_indexed | 2024-03-06T22:08:54Z |
| publishDate | 2015 |
| publisher | Association for the Advancement of Artificial Intelligence |
| record_format | dspace |
| spelling | oxford-uuid:51203886-45e9-430c-b95c-c0ea6d2386562022-03-26T16:17:38ZLogical foundations of privacy-preserving publishing of linked dataConference itemhttp://purl.org/coar/resource_type/c_5794uuid:51203886-45e9-430c-b95c-c0ea6d238656Symplectic Elements at OxfordAssociation for the Advancement of Artificial Intelligence2015Cuenca Grau, BKostylev, E<p>The widespread adoption of Linked Data has been driven by the increasing demand for information exchange between organisations, as well as by data publishing regulations in domains such as health care and governance. In this setting, sensitive information is at risk of disclosure since published data can be linked with arbitrary external data sources</p> <p>In this paper we lay the foundations of privacy-preserving data publishing (PPDP) in the context of Linked Data. We consider anonymisations of RDF graphs (and, more generally, relational datasets with labelled nulls) and define notions of safe and optimal anonymisations. Safety ensures that the anonymised data can be published with provable protection guarantees against linking attacks, whereas optimality ensures that it preserves as much information from the original data as possible, while satisfying the safety requirement. We establish the complexity of the underpinning decision problems both under open-world semantics inherent to RDF and a closed-world semantics, where we assume that an attacker has complete knowledge over some part of the original data.</p> |
| spellingShingle | Cuenca Grau, B Kostylev, E Logical foundations of privacy-preserving publishing of linked data |
| title | Logical foundations of privacy-preserving publishing of linked data |
| title_full | Logical foundations of privacy-preserving publishing of linked data |
| title_fullStr | Logical foundations of privacy-preserving publishing of linked data |
| title_full_unstemmed | Logical foundations of privacy-preserving publishing of linked data |
| title_short | Logical foundations of privacy-preserving publishing of linked data |
| title_sort | logical foundations of privacy preserving publishing of linked data |
| work_keys_str_mv | AT cuencagraub logicalfoundationsofprivacypreservingpublishingoflinkeddata AT kostyleve logicalfoundationsofprivacypreservingpublishingoflinkeddata |