Dynamic Node Privacy Feature Decoupling Graph Autoencoder Based on Attention Mechanism
Graph autoencoders’ inherent capability to capture node feature correlations poses significant privacy risks through attackers inference. Previous feature decoupling approaches predominantly apply uniform privacy protection across nodes, disregarding the varying sensitivity levels inherent in graph...
Uloženo v:
| Vydáno v: | Applied sciences Ročník 15; číslo 12; s. 6489 |
|---|---|
| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Basel
MDPI AG
01.06.2025
|
| Témata: | |
| ISSN: | 2076-3417, 2076-3417 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Graph autoencoders’ inherent capability to capture node feature correlations poses significant privacy risks through attackers inference. Previous feature decoupling approaches predominantly apply uniform privacy protection across nodes, disregarding the varying sensitivity levels inherent in graph structures. To solve the above problems, we propose a novel dual-path graph autoencoder incorporating attention-aware privacy adaptation. Firstly, we design an attention-driven metric learning framework to quantify node-specific privacy importance through attention weights and select important nodes to construct the privacy distribution, so that realizing the dynamically privacy decoupling and reducing utility loss. Then, we introduce Hilbert-Schmidt Independence Criterion (HSIC) to measure the dependence between privacy and non-privacy information, which avoids the deviations that occur when using approximate methods such as variational inference. Finally, we use the method of alternating training to comprehensively evaluate the privacy importance of nodes. Experimental results on three real-world datasets—Yale, Rochester, and Credit Defaulter—demonstrate that our proposed method significantly outperforms existing approaches like PVGAE, GAE-MI, and APGE, where the inference accuracy regarding privacy decreased by 25.5%, but the accuracy rate of link prediction achieved the highest 84.7% compared to other methods. |
|---|---|
| AbstractList | Graph autoencoders’ inherent capability to capture node feature correlations poses significant privacy risks through attackers inference. Previous feature decoupling approaches predominantly apply uniform privacy protection across nodes, disregarding the varying sensitivity levels inherent in graph structures. To solve the above problems, we propose a novel dual-path graph autoencoder incorporating attention-aware privacy adaptation. Firstly, we design an attention-driven metric learning framework to quantify node-specific privacy importance through attention weights and select important nodes to construct the privacy distribution, so that realizing the dynamically privacy decoupling and reducing utility loss. Then, we introduce Hilbert-Schmidt Independence Criterion (HSIC) to measure the dependence between privacy and non-privacy information, which avoids the deviations that occur when using approximate methods such as variational inference. Finally, we use the method of alternating training to comprehensively evaluate the privacy importance of nodes. Experimental results on three real-world datasets—Yale, Rochester, and Credit Defaulter—demonstrate that our proposed method significantly outperforms existing approaches like PVGAE, GAE-MI, and APGE, where the inference accuracy regarding privacy decreased by 25.5%, but the accuracy rate of link prediction achieved the highest 84.7% compared to other methods. |
| Audience | Academic |
| Author | Tang, Jinchuan Dang, Shuping Huang, Yikai |
| Author_xml | – sequence: 1 givenname: Yikai surname: Huang fullname: Huang, Yikai – sequence: 2 givenname: Jinchuan surname: Tang fullname: Tang, Jinchuan – sequence: 3 givenname: Shuping surname: Dang fullname: Dang, Shuping |
| BookMark | eNpNUctuFDEQtFCQCCEnfsASR7TBzxn7uCQkRAokB-Bq9dg9G6927cHjQdq_j8kilO5Dt0rVpVLXW3KSckJC3nN2IaVln2CauOaiU8a-IqeC9d1KKt6fvNjfkPN53rJWlkvD2Sn5dXVIsI-efs8B6UOJf8Af6DVCXQrSK_R5mXYxbehNgemRrpeaMfnGLfQzzBhoTnRdK6Ya2_YN_SOkOO_fkdcj7GY8_zfPyM_rLz8uv67u7m9uL9d3Ky87WVe91Ur1oFCwIQzDqEEzEZQFAyAM91xC50evlO1UkHqUtu9MY_c2GDaMgzwjt0fdkGHrphL3UA4uQ3TPQC4bB6VGv0NnlQRQelSqvUgFaxQwwCC4DygkYtP6cNSaSv694FzdNi8lNftOCiFNL4WWjXVxZG2gicY05lrAtw7Y3tgSGWPD10Zp3TNjdDv4eDzwJc9zwfG_Tc7c3-Dci-DkE1kJi4I |
| Cites_doi | 10.1142/S0218488502001648 10.1016/j.neucom.2024.129001 10.1007/11787006_1 10.1145/3534678.3539321 10.1145/1055558.1055591 10.1145/3534678.3539302 10.1145/3583780.3614933 10.1007/s00355-023-01456-4 10.1016/j.knosys.2021.107567 10.1109/JIOT.2020.3036583 10.1109/TDSC.2024.3417513 10.1145/3447548.3467273 10.3390/su151511893 10.1145/3637528.3672013 10.1145/3437963.3441752 10.1145/3448891.3448939 10.1145/3534678.3539232 10.1007/s10207-022-00646-y 10.1109/IJCNN55064.2022.9892789 |
| ContentType | Journal Article |
| Copyright | COPYRIGHT 2025 MDPI AG 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: COPYRIGHT 2025 MDPI AG – notice: 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | AAYXX CITATION ABUWG AFKRA AZQEC BENPR CCPQU DWQXO PHGZM PHGZT PIMPY PKEHL PQEST PQQKQ PQUKI PRINS DOA |
| DOI | 10.3390/app15126489 |
| DatabaseName | CrossRef ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials - QC ProQuest Central ProQuest One ProQuest Central ProQuest Central Premium ProQuest One Academic Publicly Available Content Database (ProQuest) ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest One Academic Eastern Edition ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest Central China ProQuest Central ProQuest One Academic UKI Edition ProQuest Central Korea ProQuest Central (New) ProQuest One Academic ProQuest One Academic (New) |
| DatabaseTitleList | Publicly Available Content Database CrossRef |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Sciences (General) |
| EISSN | 2076-3417 |
| ExternalDocumentID | oai_doaj_org_article_943aa45f442644d984a0aed21cde23ee A845570885 10_3390_app15126489 |
| GroupedDBID | .4S 2XV 5VS 7XC 8CJ 8FE 8FG 8FH AADQD AAFWJ AAYXX ADBBV ADMLS AFFHD AFKRA AFPKN AFZYC ALMA_UNASSIGNED_HOLDINGS APEBS ARCSS BCNDV BENPR CCPQU CITATION CZ9 D1I D1J D1K GROUPED_DOAJ IAO IGS ITC K6- K6V KC. KQ8 L6V LK5 LK8 M7R MODMG M~E OK1 P62 PHGZM PHGZT PIMPY PROAC TUS ABUWG AZQEC DWQXO PKEHL PQEST PQQKQ PQUKI PRINS |
| ID | FETCH-LOGICAL-c363t-795447a4e20bdbbf5a502d49a8aa281c13a6cfc44964d35f397684e279d80bfb3 |
| IEDL.DBID | PIMPY |
| ISICitedReferencesCount | 0 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001515296400001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2076-3417 |
| IngestDate | Tue Oct 14 18:45:32 EDT 2025 Mon Jun 30 07:18:31 EDT 2025 Tue Nov 04 18:14:56 EST 2025 Sat Nov 29 07:12:53 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 12 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c363t-795447a4e20bdbbf5a502d49a8aa281c13a6cfc44964d35f397684e279d80bfb3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| OpenAccessLink | https://www.proquest.com/publiccontent/docview/3223873253?pq-origsite=%requestingapplication% |
| PQID | 3223873253 |
| PQPubID | 2032433 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_943aa45f442644d984a0aed21cde23ee proquest_journals_3223873253 gale_infotracacademiconefile_A845570885 crossref_primary_10_3390_app15126489 |
| PublicationCentury | 2000 |
| PublicationDate | 2025-06-01 |
| PublicationDateYYYYMMDD | 2025-06-01 |
| PublicationDate_xml | – month: 06 year: 2025 text: 2025-06-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Basel |
| PublicationPlace_xml | – name: Basel |
| PublicationTitle | Applied sciences |
| PublicationYear | 2025 |
| Publisher | MDPI AG |
| Publisher_xml | – name: MDPI AG |
| References | Sweeney (ref_8) 2002; 10 Wang (ref_5) 2023; 22 ref_14 ref_13 Lin (ref_17) 2024; 22 ref_12 ref_11 ref_10 Amara (ref_2) 2025; 619 ref_19 ref_18 ref_16 ref_15 Wang (ref_7) 2021; 234 Bloch (ref_23) 2023; 61 ref_24 ref_22 ref_21 ref_20 ref_1 ref_3 Li (ref_25) 2020; 8 ref_9 ref_4 ref_6 |
| References_xml | – volume: 10 start-page: 557 year: 2002 ident: ref_8 article-title: k-anonymity: A model for protecting privacy publication-title: Int. J. Uncertain. Fuzziness- Knowl.-Based Syst. doi: 10.1142/S0218488502001648 – volume: 619 start-page: 129001 year: 2025 ident: ref_2 article-title: A multi-view GNN-based network representation learning framework for recommendation systems publication-title: Neurocomputing doi: 10.1016/j.neucom.2024.129001 – ident: ref_10 doi: 10.1007/11787006_1 – ident: ref_11 – ident: ref_1 – ident: ref_4 doi: 10.1145/3534678.3539321 – ident: ref_9 doi: 10.1145/1055558.1055591 – ident: ref_15 doi: 10.1145/3534678.3539302 – ident: ref_20 doi: 10.1145/3583780.3614933 – ident: ref_21 – volume: 61 start-page: 413 year: 2023 ident: ref_23 article-title: Centrality measures in networks publication-title: Soc. Choice Welf. doi: 10.1007/s00355-023-01456-4 – ident: ref_6 – volume: 234 start-page: 107567 year: 2021 ident: ref_7 article-title: Learning with Hilbert–Schmidt independence criterion: A review and new perspectives publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2021.107567 – volume: 8 start-page: 6904 year: 2020 ident: ref_25 article-title: Adversarial privacy-preserving graph embedding against inference attack publication-title: IEEE Internet Things J. doi: 10.1109/JIOT.2020.3036583 – volume: 22 start-page: 967 year: 2024 ident: ref_17 article-title: Graph privacy funnel: A variational approach for privacy-preserving representation learning on graphs publication-title: IEEE Trans. Dependable Secur. Comput. doi: 10.1109/TDSC.2024.3417513 – ident: ref_24 doi: 10.1145/3447548.3467273 – ident: ref_3 doi: 10.3390/su151511893 – ident: ref_13 – ident: ref_18 doi: 10.1145/3637528.3672013 – ident: ref_14 doi: 10.1145/3437963.3441752 – ident: ref_19 doi: 10.1145/3448891.3448939 – ident: ref_16 doi: 10.1145/3534678.3539232 – ident: ref_22 – volume: 22 start-page: 497 year: 2023 ident: ref_5 article-title: Defense against membership inference attack in graph neural networks through graph perturbation publication-title: Int. J. Inf. Secur. doi: 10.1007/s10207-022-00646-y – ident: ref_12 doi: 10.1109/IJCNN55064.2022.9892789 |
| SSID | ssj0000913810 |
| Score | 2.320442 |
| Snippet | Graph autoencoders’ inherent capability to capture node feature correlations poses significant privacy risks through attackers inference. Previous feature... |
| SourceID | doaj proquest gale crossref |
| SourceType | Open Website Aggregation Database Index Database |
| StartPage | 6489 |
| SubjectTerms | attention graph autoencoder Hypothesis testing Methods Neural networks Privacy privacy decouple Privacy, Right of privacy-utility trade-off Social networks |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Nb9QwEB2higMcEC0gFgryoVLhEJHYk8Q-biltL131AKg3a5Kx0R7YrbLZSvx7PEmKckFcuEY-WG8yX7bnPYATjCnsMdmMcsMZNsyZpSZmzJUJddCVRh7EJurVyt7eupuZ1Je8CRvpgUfgPjk0RFhGHFI3O4uUU2BdtBy0CUGib6p6Zs3UEINdIdRV40CeSX293AdLcqtQBN1nKWhg6v9bPB6SzMVzeDZVh2o57uoQHoXNETydcQYeweHkjTv1YaKM_vgCvp-PwvJqteWgbrr1PbW_lJR3-y6o89Ri7mXy9oe6FH5qtdz3WyGw5NCps5TGWG03atn349NHdR1kHHi9-_kSvl18-fr5KpsUE7LWVKbPalci1oRB5w03TSypzDWjI0ukbdEWhqo2toiuQjZllGLEptW1Y5s3sTGv4GCz3YTXoBLGcu-bV6QDVqxdKgVjEXOk5OSBzAJOHkD0dyMxhk8NhWDtZ1gv4EwA_rNE2KyHD8nGfrKx_5eNF3Aq5vHic31HLU2jA2mnwl7llxaFSczacgHHDxb0kzPufIpZxtZGl-bN_9jNW3iiRQR4OIo5hoO-24d38Li979e77v3wH_4GOFPicw priority: 102 providerName: Directory of Open Access Journals |
| Title | Dynamic Node Privacy Feature Decoupling Graph Autoencoder Based on Attention Mechanism |
| URI | https://www.proquest.com/docview/3223873253 https://doaj.org/article/943aa45f442644d984a0aed21cde23ee |
| Volume | 15 |
| WOSCitedRecordID | wos001515296400001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2076-3417 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913810 issn: 2076-3417 databaseCode: DOA dateStart: 20110101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2076-3417 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913810 issn: 2076-3417 databaseCode: M~E dateStart: 20110101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2076-3417 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913810 issn: 2076-3417 databaseCode: BENPR dateStart: 20110101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2076-3417 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913810 issn: 2076-3417 databaseCode: PIMPY dateStart: 20110101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Nb9QwEB3BLgc4AC0gFsrKh0rAIWpiO4lzQru0BQ5dRQhQOUWTjFPtgU1JspX493gSb9kLnLgmlmLleT489rwHcKxr5_YITYChokCXRIHBsg6IEmVTKxOpaRCbSFcrc3mZ5b49uvPXKnc-cXDUI9sz39t2TviEmoor5iduGSqTKhmrd9c_A9aQ4rNWL6hxF6ZMvGUmMM0_XeTfb2suzIFponBs01Nut8-nxBzyEs0y73uBaeDv_5uXHkLP-aP_O-nH8NCnoGIxrpkDuGM3h_Bgj5jwEA68yXfijeelfvsEvp2O6vVi1ZAVebu-weqX4Bxy21px6vaxW27vvRIfmARbLLZ9wyyZZFuxdLGSRLMRi74f71eKC8s9x-vux1P4en725f3HwMsyBJVKVB-kmfu5KWorw5LKso4xDiXpDA2iNFEVKUyqutI6SzSpuOaMx7jRaUYmLOtSPYPJptnY5yDQEh8uhwlKqxOSmcs366gONTpPYlHN4HiHSXE9sm8UbtfC0BV70M1gyXjdDmHK7OFB014V3gKLTCtEHdd6yAEpMxpD930ZVWSlsnYGrxntgg27b7FC35_gZsoUWcXCaKYrMyaewdEO7cJbfFf8AffFv1-_hPuSNYSHSs4RTPp2a1_BveqmX3ftHKbLs1X-eT7UBuZ-Af8GPnP_mA |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3LbtQwFL0qUyTKAmgBdaCAF0XAIiKxncRZIDRlKB21M5pFQe3KOLZTzaJJSTJF81N8I755lNnArgu2iZWXT869ftxzAPZ55mjPKOEpnxmPp8Z4QqWZZ0zEbGxpRLlpzCbi2UycnSXzDfjV18LgtsqeExuiNoXGOfL3DnhMxIyG7OPVDw9do3B1tbfQaGFxbFc_3ZCt-jAZu_59Tenh59NPR17nKuBpFrHai5OQ81hxS_3UpGkWqtCnhidKKEVFoAOmIp1pzpOIGxZmGLCFax0nRvhpljJ33TuwyR3YxQA255Pp_PxmVgdVNkXgt4WAjCU-rkNjUI04Gsmvhb7GIeBvcaAJbocP_7fP8ggedGk0GbW434YNm-_A_TVxxR3Y7mirIm87be13j-HbeJWry4Ums8JYMi8X10qvCObBy9KSsRuLL7FE-YJ8QSFvMlrWBSp9GluSAxfvDSlyMqrrdo8omVqsm15Ul0_g66287VMY5EVud4Eoa3CB3I8UtTwyNHE5cxZkPleODa1iQ9jve11etQoi0o28EBxyDRxDOEBE3DRB2e_mQFFeyI5FZMKZUjzMeJPHmkRw5bv700AbS5m1Q3iDeJJITnWptOpqLNyTosyXHAmOkmtChEPY6_EkO9aq5B8wPfv36Vdw7-h0eiJPJrPj57BF0RO5mZnag0FdLu0LuKuv60VVvux-EALfbxt8vwGX5k5b |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3LbtQwFL0qU4RgAbSAGCjgRRGwiJrYTuIsEJoyDIxKR1kAalfGie1qFk1KkimaX-Pr8M2jzAZ2XbBNrDyPz71-3HMA9rl1tKeV8JTPtMczrT2hMutpHTETGxpRrluziXixECcnSboFv4ZaGNxWOXBiS9S6zHGO_MABj4mY0ZAd2H5bRDqdvbv44aGDFK60DnYaHUSOzPqnG77Vb-dT969fUjr78OX9J693GPByFrHGi5OQ81hxQ_1MZ5kNVehTzRMllKIiyAOmotzmnCcR1yy0GLyFax0nWviZzZi77g3Ydik55yPYTufH6enVDA8qborA74oCGUt8XJPGABtxNJXfCIOtW8DfYkIb6Gb3_udPdB_u9uk1mXT9YQe2TLELdzZEF3dhp6ezmrzuNbffPIBv03Whzpc5WZTakLRaXqp8TTA_XlWGTN0YfYWly2fkIwp8k8mqKVEBVJuKHLo8QJOyIJOm6faOkmOD9dTL-vwhfL2Wt30Eo6IszGMgymhcOPcjRQ2PNE1cLm0D63PlWNIoNob9AQHyolMWkW5EhkCRG0AZwyGi46oJyoG3B8rqTPbsIhPOlOKh5W1-qxPBle_uT4NcG8qMGcMrxJZE0moqlau-9sI9Kcp_yYngKMUmRDiGvQFbsmezWv4B1pN_n34Btxzi5Of54ugp3KZoldxOWO3BqKlW5hnczC-bZV097_sKge_Xjb3fOrpXHA |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Dynamic+Node+Privacy+Feature+Decoupling+Graph+Autoencoder+Based+on+Attention+Mechanism&rft.jtitle=Applied+sciences&rft.au=Huang+Yikai&rft.au=Tang+Jinchuan&rft.au=Dang+Shuping&rft.date=2025-06-01&rft.pub=MDPI+AG&rft.eissn=2076-3417&rft.volume=15&rft.issue=12&rft.spage=6489&rft_id=info:doi/10.3390%2Fapp15126489&rft.externalDBID=HAS_PDF_LINK |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2076-3417&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2076-3417&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2076-3417&client=summon |