Obfuscation: A user’s guide for privacy and protest

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Critical data studies: An introduction (2016, background): “While data are all of the above and more, they are also conspicuous in their absence—a lack of data is another indication of power, the power not to look or to remain hidden (Brunton and Nissenbaum, 2015; Flyverbom et al., 2016).”
Fairer machine learning in the real world: Mitigating discrimination without collecting sensitive data (2017, background): “…between organisations deploying such systems and those aﬀected by them, co-operation may not be feasible or desirable; aﬀected groups may instead be drawn (understandably) to more adversarial forms of resistance and political action (Brunton and Nissenbaum, 2015; Danaher, 2016; Lyon, 2007).”
Addressing Privacy Threats from Machine Learning (2021, background): “Although the above examples focus on image classification tasks, similar obfuscation tactics have been used for resisting web tracking, loyalty card-based tracking, and more (Brunton and Nissenbaum, 2015; Kulynych et al., 2020).”
The corporate cultivation of digital resignation (2019, background): “Although they note the potential for powerful players to engage in obfuscatory actions (Brunton and Nissenbaum, 2015: 9), Brunton and Nissenbaum (2015) focus on obfuscation as a strategy for citizen protection: “the deliberate addition of ambiguous, confusing, or misleading information to interfere…”