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Today, targeted online advertising relies on unique identifiers assigned tousers through third-party cookies--a practice at odds with user privacy. Whilethe web and advertising communities have proposed solutions that we refer to asinterest-disclosing mechanisms, including Google's Topics API, an independentanalysis of these proposals in realistic scenarios has yet to be performed. Inthis paper, we attempt to validate the privacy (i.e., preventing uniqueidentification) and utility (i.e., enabling ad targeting) claims of Google'sTopics proposal in the context of realistic user behavior. Through newstatistical models of the distribution of user behaviors and resultingtargeting topics, we analyze the capabilities of malicious advertisersobserving users over time and colluding with other third parties. Our analysisshows that even in the best case, individual users' identification across sitesis possible, as 0.4% of the 250k users we simulate are re-identified. Theseguarantees weaken further over time and when advertisers collude: 57% of userswith stable interests are uniquely re-identified when their browsing activityhas been observed for 15 epochs, increasing to 75% after 30 epochs. Whilemeasuring that the Topics API provides moderate utility, we also find thatadvertisers and publishers can abuse the Topics API to potentially assignunique identifiers to users, defeating the desired privacy guarantees. As aresult, the inherent diversity of users' interests on the web is directly atodds with the privacy objectives of interest-disclosing mechanisms; we discusshow any replacement of third-party cookies may have to seek other avenues toachieve privacy for the web.

Interest-disclosing Mechanisms for Advertising are Privacy-Exposing (not Preserving)