{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/91596"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/91596","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Privacy risk and de-anonymization in heterogeneous information networks","abstract":"Anonymized user datasets are often released for research or industry applications. As an example, t.qq.com released its anonymized users’ profile, social interaction, and recommendation log data in KDD Cup 2012 to call for recommendation algorithms. Since the entities (users and so on) and edges (links among entities) are of multiple types, the released social network is a heterogeneous information network. Prior work has shown how privacy can be compromised in homogeneous information networks by the use of specific types of graph patterns. We show how the extra information derived from heterogeneity can be used to relax these assumptions. To characterize and demonstrate this added threat, we formally define privacy risk in an anonymized heterogeneous information network to identify the vulnerability in the possible way such data are released, and further present a new de-anonymization attack that exploits the vulnerability. Our attack successfully de-anonymized most individuals involved in the data. We further show that the general ideas of exploiting privacy risk and de-anonymizing heterogeneous information networks can be extended to more general graphs.","abstract_html":"Anonymized user datasets are often released for research or industry applications. As an example, t.qq.com released its anonymized users’ profile, social interaction, and recommendation log data in KDD Cup 2012 to call for recommendation algorithms. Since the entities (users and so on) and edges (links among entities) are of multiple types, the released social network is a heterogeneous information network. Prior work has shown how privacy can be compromised in homogeneous information networks by the use of specific types of graph patterns. We show how the extra information derived from heterogeneity can be used to relax these assumptions. To characterize and demonstrate this added threat, we formally define privacy risk in an anonymized heterogeneous information network to identify the vulnerability in the possible way such data are released, and further present a new de-anonymization attack that exploits the vulnerability. Our attack successfully de-anonymized most individuals involved in the data. We further show that the general ideas of exploiting privacy risk and de-anonymizing heterogeneous information networks can be extended to more general graphs.","abstract_has_math":false,"creators":["Zhang, Aston"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Gunter, Carl A.","Han, Jiawei"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-09-09T21:14:41Z","date_published":"2016-09-09T21:14:41Z","updated_at":"2026-07-22T22:26:34Z","subjects":["Privacy, Information Networks"],"languages":["en"],"rights":["Copyright 2015 Aston Zhang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/91596","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Gunter, Carl A.","Han, Jiawei"]},{"key":"dc:creator","label":"Author","values":["Zhang, Aston"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2016-09-09T21:14:41Z","2018-09-10T09:15:23Z","2015-07-22","2015-08"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Privacy, Information Networks"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2015 Aston Zhang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/91596"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Anonymized user datasets are often released for research or industry applications. As an example, t.qq.com released its anonymized users’ profile, social interaction, and recommendation log data in KDD Cup 2012 to call for recommendation algorithms. Since the entities (users and so on) and edges (links among entities) are of multiple types, the released social network is a heterogeneous information network. Prior work has shown how privacy can be compromised in homogeneous information networks by the use of specific types of graph patterns. We show how the extra information derived from heterogeneity can be used to relax these assumptions. To characterize and demonstrate this added threat, we formally define privacy risk in an anonymized heterogeneous information network to identify the vulnerability in the possible way such data are released, and further present a new de-anonymization attack that exploits the vulnerability. Our attack successfully de-anonymized most individuals involved in the data. We further show that the general ideas of exploiting privacy risk and de-anonymizing heterogeneous information networks can be extended to more general graphs.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2017-08-01","The student, Aston Zhang, accepted the attached license on 2015-07-15 at 17:58.","The student, Aston Zhang, submitted this Thesis for approval on 2015-07-15 at 18:01.","This Thesis was approved for publication on 2015-07-22 at 16:17.","DSpace SAF Submission Ingestion Package generated from Vireo submission #8487 on 2016-09-09 at 16:06:55","Made available in DSpace on 2016-09-09T21:14:41Z (GMT). No. of bitstreams: 2 ZHANG-THESIS-2015.pdf: 1411196 bytes, checksum: 73307da46877d60fd15b5fab6328e796 (MD5) LICENSE.txt: 4208 bytes, checksum: d2d861810d3db683b4353db429a34472 (MD5) Previous issue date: 2015-07-22","Embargo set by: Seth Robbins for item 93980 Lift date: 2018-09-09T21:14:46Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Limited Restriction Lifted for Item 93980 on 2018-09-10T09:15:23Z."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Privacy risk and de-anonymization in heterogeneous information networks"]}]}],"canonical_facts":{"dc:contributor":["Gunter, Carl A.","Han, Jiawei"],"dc:creator":["Zhang, Aston"],"dc:date":["2016-09-09T21:14:41Z","2018-09-10T09:15:23Z","2015-07-22","2015-08"],"dc:description":["Anonymized user datasets are often released for research or industry applications. As an example, t.qq.com released its anonymized users’ profile, social interaction, and recommendation log data in KDD Cup 2012 to call for recommendation algorithms. Since the entities (users and so on) and edges (links among entities) are of multiple types, the released social network is a heterogeneous information network. Prior work has shown how privacy can be compromised in homogeneous information networks by the use of specific types of graph patterns. We show how the extra information derived from heterogeneity can be used to relax these assumptions. To characterize and demonstrate this added threat, we formally define privacy risk in an anonymized heterogeneous information network to identify the vulnerability in the possible way such data are released, and further present a new de-anonymization attack that exploits the vulnerability. Our attack successfully de-anonymized most individuals involved in the data. We further show that the general ideas of exploiting privacy risk and de-anonymizing heterogeneous information networks can be extended to more general graphs.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2017-08-01","The student, Aston Zhang, accepted the attached license on 2015-07-15 at 17:58.","The student, Aston Zhang, submitted this Thesis for approval on 2015-07-15 at 18:01.","This Thesis was approved for publication on 2015-07-22 at 16:17.","DSpace SAF Submission Ingestion Package generated from Vireo submission #8487 on 2016-09-09 at 16:06:55","Made available in DSpace on 2016-09-09T21:14:41Z (GMT). No. of bitstreams: 2 ZHANG-THESIS-2015.pdf: 1411196 bytes, checksum: 73307da46877d60fd15b5fab6328e796 (MD5) LICENSE.txt: 4208 bytes, checksum: d2d861810d3db683b4353db429a34472 (MD5) Previous issue date: 2015-07-22","Embargo set by: Seth Robbins for item 93980 Lift date: 2018-09-09T21:14:46Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Limited Restriction Lifted for Item 93980 on 2018-09-10T09:15:23Z."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/91596"],"dc:language":["en"],"dc:rights":["Copyright 2015 Aston Zhang"],"dc:subject":["Privacy, Information Networks"],"dc:title":["Privacy risk and de-anonymization in heterogeneous information networks"],"dc:type":["text"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:26:34Z"}