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Online tracking poses a serious privacy challenge that has drawn significant attention in both academia and industry. Existing approaches for preventing user tracking, based on curated blocklists, suffer from limited coverage and coarse-grained resolution for classification, rely on exceptions that impact sites' functionality and appearance, and require significant manual maintenance. In this paper we propose a novel approach, based on the concepts leveraged from k-Anonymity, in which users collectively identify unsafe data elements, which have the potential to identify uniquely an individual user, and remove them from requests.
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Yu et al. (2016) studied this question.
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