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arXiv:2110.06636cs.SI

Unique on Facebook: Formulation and Evidence of (Nano)targeting Individual Users with non-PII Data

José González-Cabañas, Ángel Cuevas, Rubén Cuevas, Juan López-Fernández, David García

Abstract

The privacy of an individual is bounded by the ability of a third party to reveal their identity. Certain data items such as a passport ID or a mobile phone number may be used to uniquely identify a person. These are referred to as Personal Identifiable Information (PII) items. Previous literature has also reported that, in datasets including millions of users, a combination of several non-PII items (which alone are not enough to identify an individual) can uniquely identify an individual within the dataset. In this paper, we define a data-driven model to quantify the number of interests from a user that make them unique on Facebook. To the best of our knowledge, this represents the first study of individuals' uniqueness at the world population scale. Besides, users' interests are actionable non-PII items that can be used to define ad campaigns and deliver tailored ads to Facebook users. We run an experiment through 21 Facebook ad campaigns that target three of the authors of this paper to prove that, if an advertiser knows enough interests from a user, the Facebook Advertising Platform can be systematically exploited to deliver ads exclusively to a specific user. We refer to this practice as nanotargeting. Finally, we discuss the harmful risks associated with nanotargeting such as psychological persuasion, user manipulation, or blackmailing, and provide easily implementable countermeasures to preclude attacks based on nanotargeting campaigns on Facebook.

arXiv comments
16 pages, 12 figures, 4 tables
Journal reference
ACM Internet Measurement Conference (IMC '21), November 2--4, 2021, Virtual Event, USA
DOI
doi:10.1145/3487552.3487861
Subjects
cs.SISocial and Information Networks cs.CYComputers and Society

On Hacker News

Linked by 2 identifiable commenters in 2 comments across 1 thread, from Oct 2021 to Oct 2021. Found in 2 HN submissions from Oct 2021 to Oct 2021. Open submission discussions.

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Discussed in comments1 thread

    • Here’s the link to the paper on arXiv: https://arxiv.org/abs/2110.06636 as I didn’t find it in the TechCrunch article.

      yellow_postitView on HNParent context

    • Reading the article on which this is based (https://arxiv.org/abs/2110.06636), the researchers don't appear to specify how they randomly select the 25 interests from the interests assigned to a user. This could have a considerable effect on their results, leading to a number…

      nemoniacView on HNParent context

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