What can you learn from an IP?

Simran Patil, Nikita Borisov

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 can otherwise be exploited by eavesdroppers to infer users' data. But encryption may not necessarily guarantee privacy, especially when it comes to metadata. Emerging standards can protect the contents of both DNS queries and the TLS SNI extensions; however, it might still be possible to determine which websites users are visiting by simply looking at the destination IP addresses on the traffic originating from users' devices. We perform a measurement study to determine the anonymity provided by IP addresses resulting from the multiple sub-queries that are made as a consequence of accessing a particular web page. We show that, in most cases, an adversary can use the IP addresses during a page load as a form of a fingerprint to infer the original site identity.

Original languageEnglish (US)
Title of host publicationANRW 2019 - Proceedings of the 2019 Applied Networking Research Workshop
PublisherAssociation for Computing Machinery, Inc
Pages45-51
Number of pages7
ISBN (Electronic)9781450368483
DOIs
StatePublished - Jul 22 2019
Event2019 Applied Networking Research Workshop, ANRW 2019 - Montreal, Canada
Duration: Jul 22 2019 → …

Publication series

NameANRW 2019 - Proceedings of the 2019 Applied Networking Research Workshop

Conference

Conference2019 Applied Networking Research Workshop, ANRW 2019
CountryCanada
CityMontreal
Period7/22/19 → …

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems

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  • Cite this

    Patil, S., & Borisov, N. (2019). What can you learn from an IP? In ANRW 2019 - Proceedings of the 2019 Applied Networking Research Workshop (pp. 45-51). (ANRW 2019 - Proceedings of the 2019 Applied Networking Research Workshop). Association for Computing Machinery, Inc. https://doi.org/10.1145/3340301.3341133