On the Robustness of Topics API to a Re-Identification Attack
- URL: http://arxiv.org/abs/2306.05094v1
- Date: Thu, 8 Jun 2023 10:53:48 GMT
- Title: On the Robustness of Topics API to a Re-Identification Attack
- Authors: Nikhil Jha, Martino Trevisan, Emilio Leonardi, Marco Mellia
- Abstract summary: Google proposed the Topics API framework as a privacy-friendly alternative for behavioural advertising.
This paper evaluates the robustness of the Topics API to a re-identification attack.
We find that the Topics API mitigates but cannot prevent re-identification to take place, as there is a sizeable chance that a user's profile is unique within a website's audience.
- Score: 6.157783777246449
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Web tracking through third-party cookies is considered a threat to users'
privacy and is supposed to be abandoned in the near future. Recently, Google
proposed the Topics API framework as a privacy-friendly alternative for
behavioural advertising. Using this approach, the browser builds a user profile
based on navigation history, which advertisers can access. The Topics API has
the possibility of becoming the new standard for behavioural advertising, thus
it is necessary to fully understand its operation and find possible
limitations.
This paper evaluates the robustness of the Topics API to a re-identification
attack where an attacker reconstructs the user profile by accumulating user's
exposed topics over time to later re-identify the same user on a different
website. Using real traffic traces and realistic population models, we find
that the Topics API mitigates but cannot prevent re-identification to take
place, as there is a sizeable chance that a user's profile is unique within a
website's audience. Consequently, the probability of correct re-identification
can reach 15-17%, considering a pool of 1,000 users. We offer the code and data
we use in this work to stimulate further studies and the tuning of the Topic
API parameters.
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