{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/84538"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/84538","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Privacy -Enhancing Data Mining: Issues, Techniques and Measures","abstract":"The study presents some effective privacy-enhancing transformation techniques that are applicable to various data types. The techniques are able to retain privacy while accessing the information contained in the original data. Specifically, we address the issue of privacy protection through using the data filter, partitioning, synthetic data, and randomization methods. We give examples of inducing the decision-tree classifiers and building detection models of fraud from training data in which the values of sensitive attribute values have been modified. We experimentally validate the privacy-enhancing techniques and the measurement methodology over both real world and synthetic datasets. The experimental results show that the application of privacy-enhancing techniques can preserve the data privacy with minimum loss of information. The results also demonstrate that the proposed techniques can achieve comparative performance measures or mining results while preserving the data privacy.","abstract_html":"The study presents some effective privacy-enhancing transformation techniques that are applicable to various data types. The techniques are able to retain privacy while accessing the information contained in the original data. Specifically, we address the issue of privacy protection through using the data filter, partitioning, synthetic data, and randomization methods. We give examples of inducing the decision-tree classifiers and building detection models of fraud from training data in which the values of sensitive attribute values have been modified. We experimentally validate the privacy-enhancing techniques and the measurement methodology over both real world and synthetic datasets. The experimental results show that the application of privacy-enhancing techniques can preserve the data privacy with minimum loss of information. The results also demonstrate that the proposed techniques can achieve comparative performance measures or mining results while preserving the data privacy.","abstract_has_math":false,"creators":["Li, Jingquan"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Business Administration","degree_department":null,"school":null,"contributors":["Shaw, Michael J."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-09-25T22:21:32Z","date_published":"2015-09-25T22:21:32Z","updated_at":"2026-07-22T22:26:23Z","subjects":["Business Administration, Management"],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["(MiAaPQ)AAI3160914"],"render_values":[{"text":"(MiAaPQ)AAI3160914","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/84538","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Shaw, Michael J."]},{"key":"dc:creator","label":"Author","values":["Li, Jingquan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2015-09-25T22:21:32Z","10000-01-01","2004"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Business Administration"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"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":["Business Administration, Management"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/84538","(MiAaPQ)AAI3160914"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The study presents some effective privacy-enhancing transformation techniques that are applicable to various data types. The techniques are able to retain privacy while accessing the information contained in the original data. Specifically, we address the issue of privacy protection through using the data filter, partitioning, synthetic data, and randomization methods. We give examples of inducing the decision-tree classifiers and building detection models of fraud from training data in which the values of sensitive attribute values have been modified. We experimentally validate the privacy-enhancing techniques and the measurement methodology over both real world and synthetic datasets. The experimental results show that the application of privacy-enhancing techniques can preserve the data privacy with minimum loss of information. The results also demonstrate that the proposed techniques can achieve comparative performance measures or mining results while preserving the data privacy.","Made available in DSpace on 2015-09-25T22:21:32Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 3160914.pdf: 5640815 bytes, checksum: 9c62e7c6da47d69ba52db6a2acdb064e (MD5) Previous issue date: 2004","Embargo set by: Seth Robbins for item 85819 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs","Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs","U of I Only","121 p.","Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2004."]},{"key":"dc:title","label":"Title","values":["Privacy -Enhancing Data Mining: Issues, Techniques and Measures"]}]}],"canonical_facts":{"dc:contributor":["Shaw, Michael J."],"dc:creator":["Li, Jingquan"],"dc:date":["2015-09-25T22:21:32Z","10000-01-01","2004"],"dc:description":["The study presents some effective privacy-enhancing transformation techniques that are applicable to various data types. The techniques are able to retain privacy while accessing the information contained in the original data. Specifically, we address the issue of privacy protection through using the data filter, partitioning, synthetic data, and randomization methods. We give examples of inducing the decision-tree classifiers and building detection models of fraud from training data in which the values of sensitive attribute values have been modified. We experimentally validate the privacy-enhancing techniques and the measurement methodology over both real world and synthetic datasets. The experimental results show that the application of privacy-enhancing techniques can preserve the data privacy with minimum loss of information. The results also demonstrate that the proposed techniques can achieve comparative performance measures or mining results while preserving the data privacy.","Made available in DSpace on 2015-09-25T22:21:32Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 3160914.pdf: 5640815 bytes, checksum: 9c62e7c6da47d69ba52db6a2acdb064e (MD5) Previous issue date: 2004","Embargo set by: Seth Robbins for item 85819 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs","Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs","U of I Only","121 p.","Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2004."],"dc:identifier":["http://hdl.handle.net/2142/84538","(MiAaPQ)AAI3160914"],"dc:language":["eng"],"dc:subject":["Business Administration, Management"],"dc:title":["Privacy -Enhancing Data Mining: Issues, Techniques and Measures"],"dc:type":["text"],"thesis:degree_discipline":["Business Administration"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:26:23Z"}