{"doi":"10.1609/aaai.v30i1.10105","title":"Logical Foundations of Privacy-Preserving Publishing of Linked Data","abstract":"<jats:p>\n      \n        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. 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.\n      \n    </jats:p>","journal":"Proceedings of the AAAI Conference on Artificial Intelligence","year":2016,"id":635936,"datarank":0.40620753016533157,"base_score":2.70805020110221,"endowment":2.70805020110221,"self_citation_contribution":0.40620753016533157,"citation_network_contribution":0.0,"self_endowment_contribution":0.40620753016533157,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":14,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1650114,"name":"Egor Kostylev","orcid":null,"position":1,"is_corresponding":false},{"id":1650113,"name":"Bernardo Cuenca Grau","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Logical Foundations of Privacy-Preserving Publishing of Linked Data","abstract":"<jats:p>\n      \n        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. 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.\n      \n    </jats:p>","is_dataset_classified":null,"base_score":2.70805020110221,"endowment":2.70805020110221,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19910364","pmcid":null,"openalex_id":"https://openalex.org/W2240115122","authors":[],"funders":[],"total_grants":0,"fwci":0.7765,"citation_percentile":0.72294486,"influential_citations":0,"citation_trend":[{"year":2017,"count":1},{"year":2018,"count":1},{"year":2019,"count":2},{"year":2020,"count":3},{"year":2021,"count":4},{"year":2024,"count":1},{"year":2025,"count":2}],"oa_status":"gold","license":null,"oa_locations":[{"url":"https://doi.org/10.1609/aaai.v30i1.10105","host_type":"conference"},{"url":"https://doi.org/10.1609/aaai.v30i1.10105","host_type":"publisher"},{"url":"https://ojs.aaai.org/index.php/AAAI/article/download/10105/9964","host_type":"publisher"},{"url":"https://ojs.aaai.org/index.php/AAAI/article/view/10105","host_type":""}],"fields_of_study":["Privacy-Preserving Technologies in Data","Semantic Web and Ontologies","Access Control and Trust"],"mesh_terms":[],"keywords":["Data publishing","Linked data","RDF","Computer science","Context (archaeology)","Publishing","Semantics (computer science)","Data exchange","Open data","Semantic Web","Data anonymization","Data science","Computer security","Information privacy","Information retrieval","World Wide Web","Programming language","Political science"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Peace, Justice and strong institutions"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-06T15:51:04.257537Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}