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      Does Facebook Use Sensitive Data for Advertising Purposes? Worldwide Analysis and GDPR Impact

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          Abstract

          The recent European General Data Protection Regulation (GDPR) and other data protection regulations restrict the processing of some categories of personal data (health, political orientation, sexual preferences, religious beliefs, ethnic origin, etc.) due to the privacy risks associated to such information. The GDPR refers to these categories as sensitive personal data. This paper quantifies the portion of Facebook (FB) users, across 197 countries, who are labeled with advertising interests linked to potentially sensitive personal data. Our study reveals that Facebook labels 67% of users with potential sensitive interests. This corresponds to 22% of the population in the referred 197 countries. Moreover, our work shows that the GDPR enforcement had a negligible impact in this context since the portion of FB users labeled with sensitive interests in the European Union remains almost the same 5 months before and 9 months after the GDPR was enacted. The paper also illustrates potential risks associated to the use of sensitive interests. For instance, we quantify the portion of FB users labelled with the interest "Homosexuality" in countries where being gay may be punished with the death penalty. The last contribution is the implementation of a web browser extension that allows FB users removing in a simple way the potentially sensitive interests FB has assigned them.

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          The state of phishing attacks

          Jason Hong (2012)
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            I always feel like somebody's watching me : measuring online behavioural advertising

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              Theoretical bounds of majority voting performance for a binary classification problem.

              A number of earlier studies that have attempted a theoretical analysis of majority voting assume independence of the classifiers. We formulate the majority voting problem as an optimization problem with linear constraints. No assumptions on the independence of classifiers are made. For a binary classification problem, given the accuracies of the classifiers in the team, the theoretical upper and lower bounds for performance obtained by combining them through majority voting are shown to be solutions of the corresponding optimization problem. The objective function of the optimization problem is nonlinear in the case of an even number of classifiers when rejection is allowed, for the other cases the objective function is linear and hence the problem is a linear program (LP). Using the framework we provide some insights and investigate the relationship between two candidate classifier diversity measures and majority voting performance.
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                Author and article information

                Journal
                23 July 2019
                Article
                1907.10672
                a8a1fe51-22b7-47ee-87e3-ff6500247b3e

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                Custom metadata
                6 pages, 3 figures, 3 tables. arXiv admin note: text overlap with arXiv:1802.05030
                cs.SI cs.CY

                Social & Information networks,Applied computer science
                Social & Information networks, Applied computer science

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