{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/108011"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/108011","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Privacy implications of information leakage from IP addresses - a web fingerprinting approach","abstract":"The Internet was not designed with security in mind. A number of recent protocols such as Encrypted DNS, HTTPS, etc. target encrypting critical parts of the web architecture, which were previously sent in the clear. IP addresses still remain visible to on-path observers and can be utilized for censorship, surveillance and sabotaging user’s privacy on the web. We perform a measurement study on datasets representative of the state of the Internet fetched via HTTP Archive or those collected with configurations like Adblock enabled vs. disabled over extended periods of time by crawling Alexa’s top websites to gauge the amount of information leaked by IP addresses. We build a page load fingerprint for each of the websites crawled and filter the websites that have uniquely identifying IP addresses mapped to them. We build a neural network to study how accurately the classifier works in fingerprinting websites based on IP addresses and their respective Autonomous System Numbers (ASNs). Approximately 80% of the IP addresses have an anonymity set comprising of a unique website and can successfully identify it. The classifier performs with an accuracy of about 60% on the remaining data. We observe that the classifier confuses websites belonging to common hosting infrastructures. Manual clustering efforts on the data based on these trends can increase the classification accuracy. We find areas of improvement for the current measurement study and provide suggestions to Content Delivery Networks (CDNs) and other agents fundamental to the Internet infrastructure to increase user privacy.","abstract_html":"The Internet was not designed with security in mind. A number of recent protocols such as Encrypted DNS, HTTPS, etc. target encrypting critical parts of the web architecture, which were previously sent in the clear. IP addresses still remain visible to on-path observers and can be utilized for censorship, surveillance and sabotaging user’s privacy on the web. We perform a measurement study on datasets representative of the state of the Internet fetched via HTTP Archive or those collected with configurations like Adblock enabled vs. disabled over extended periods of time by crawling Alexa’s top websites to gauge the amount of information leaked by IP addresses. We build a page load fingerprint for each of the websites crawled and filter the websites that have uniquely identifying IP addresses mapped to them. We build a neural network to study how accurately the classifier works in fingerprinting websites based on IP addresses and their respective Autonomous System Numbers (ASNs). Approximately 80% of the IP addresses have an anonymity set comprising of a unique website and can successfully identify it. The classifier performs with an accuracy of about 60% on the remaining data. We observe that the classifier confuses websites belonging to common hosting infrastructures. Manual clustering efforts on the data based on these trends can increase the classification accuracy. We find areas of improvement for the current measurement study and provide suggestions to Content Delivery Networks (CDNs) and other agents fundamental to the Internet infrastructure to increase user privacy.","abstract_has_math":false,"creators":["Patil, Simran Pramod"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Borisov, Nikita"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-08-26T21:54:58Z","date_published":"2020-08-26T21:54:58Z","updated_at":"2026-07-22T22:24:47Z","subjects":["Web Fingerprinting, Encrypted DNS, Web Privacy"],"languages":["en"],"rights":["Copyright 2020 Simran Patil"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/108011","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Borisov, Nikita"]},{"key":"dc:creator","label":"Author","values":["Patil, Simran Pramod"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2020-08-26T21:54:58Z","2020-05-11","2020-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"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":["Web Fingerprinting, Encrypted DNS, Web Privacy"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2020 Simran Patil"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/108011"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The Internet was not designed with security in mind. A number of recent protocols such as Encrypted DNS, HTTPS, etc. target encrypting critical parts of the web architecture, which were previously sent in the clear. IP addresses still remain visible to on-path observers and can be utilized for censorship, surveillance and sabotaging user’s privacy on the web. We perform a measurement study on datasets representative of the state of the Internet fetched via HTTP Archive or those collected with configurations like Adblock enabled vs. disabled over extended periods of time by crawling Alexa’s top websites to gauge the amount of information leaked by IP addresses. We build a page load fingerprint for each of the websites crawled and filter the websites that have uniquely identifying IP addresses mapped to them. We build a neural network to study how accurately the classifier works in fingerprinting websites based on IP addresses and their respective Autonomous System Numbers (ASNs). Approximately 80% of the IP addresses have an anonymity set comprising of a unique website and can successfully identify it. The classifier performs with an accuracy of about 60% on the remaining data. We observe that the classifier confuses websites belonging to common hosting infrastructures. Manual clustering efforts on the data based on these trends can increase the classification accuracy. We find areas of improvement for the current measurement study and provide suggestions to Content Delivery Networks (CDNs) and other agents fundamental to the Internet infrastructure to increase user privacy.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2020-08-25 without embargo terms","The student, Simran Patil, accepted the attached license on 2020-05-07 at 15:27.","The student, Simran Patil, submitted this Thesis for approval on 2020-05-07 at 15:30.","This Thesis was approved for publication on 2020-05-11 at 12:06.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15279 on 2020-08-25 at 17:13:09","Made available in DSpace on 2020-08-26T21:54:58Z (GMT). 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IP addresses still remain visible to on-path observers and can be utilized for censorship, surveillance and sabotaging user’s privacy on the web. We perform a measurement study on datasets representative of the state of the Internet fetched via HTTP Archive or those collected with configurations like Adblock enabled vs. disabled over extended periods of time by crawling Alexa’s top websites to gauge the amount of information leaked by IP addresses. We build a page load fingerprint for each of the websites crawled and filter the websites that have uniquely identifying IP addresses mapped to them. We build a neural network to study how accurately the classifier works in fingerprinting websites based on IP addresses and their respective Autonomous System Numbers (ASNs). Approximately 80% of the IP addresses have an anonymity set comprising of a unique website and can successfully identify it. The classifier performs with an accuracy of about 60% on the remaining data. We observe that the classifier confuses websites belonging to common hosting infrastructures. Manual clustering efforts on the data based on these trends can increase the classification accuracy. We find areas of improvement for the current measurement study and provide suggestions to Content Delivery Networks (CDNs) and other agents fundamental to the Internet infrastructure to increase user privacy.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2020-08-25 without embargo terms","The student, Simran Patil, accepted the attached license on 2020-05-07 at 15:27.","The student, Simran Patil, submitted this Thesis for approval on 2020-05-07 at 15:30.","This Thesis was approved for publication on 2020-05-11 at 12:06.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15279 on 2020-08-25 at 17:13:09","Made available in DSpace on 2020-08-26T21:54:58Z (GMT). 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