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Don’t Count Me Out: On the Relevance of IP Address in the Tracking Ecosystem

Published:20 April 2020Publication History

ABSTRACT

Targeted online advertising has become an inextricable part of the way Web content and applications are monetized. At the beginning, online advertising consisted of simple ad-banners broadly shown to website visitors. Over time, it evolved into a complex ecosystem that tracks and collects a wealth of data to learn user habits and show targeted and personalized ads. To protect users against tracking, several countermeasures have been proposed, ranging from browser extensions that leverage filter lists, to features natively integrated into popular browsers like Firefox and Brave to combat more modern techniques like browser fingerprinting. Nevertheless, few browsers offer protections against IP address-based tracking techniques. Notably, the most popular browsers, Chrome, Firefox, Safari and Edge do not offer any.

In this paper, we study the stability of the public IP addresses a user device uses to communicate with our server. Over time, a same device communicates with our server using a set of distinct IP addresses, but we find that devices reuse some of their previous IP addresses for long periods of time. We call this IP address retention and, the duration for which an IP address is retained by a device, is named the IP address retention period.

We present an analysis of 34,488 unique public IP addresses collected from 2,230 users over a period of 111 days and we show that IP addresses remain a prime vector for online tracking. 87 % of participants retain at least one IP address for more than a month and 45 % of ISPs in our dataset allow keeping the same IP address for more than 30 days. Furthermore, we also detect the presence of cycles of IP addresses in a user’s history and highlight their potential to be abused to infer traits of the user behaviour, as well as mobility traces. Our findings paint a bleak picture of the current state of online tracking at a time where IP addresses are overlooked compared to other techniques like cookies or fingerprinting.

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        • Published in

          WWW '20: Proceedings of The Web Conference 2020
          April 2020
          3143 pages
          ISBN:9781450370233
          DOI:10.1145/3366423

          Copyright © 2020 ACM

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          Publication History

          • Published: 20 April 2020

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