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Workshop: Disinformation Countermeasures and Machine Learning (DisCoML)

Privacy, Security, and Obfuscation in Reporting Technologies

Benjamin Laufer


Abstract:

Crowdsourced reporting technologies rely on the public to provide critical information for public decision-making. This work examines security, privacy, and obfuscation in the context of reporting technologies. We show that widespread use of reporting platforms comes with unique security and privacy implications, and introduce a parallel threat model and corresponding taxonomy to outline some of the many attack vectors in this space. We then perform an empirical analysis of a dataset of call logs from a controversial, real-world reporting hotline and identify coordinated obfuscation strategies that are intended to hinder the platform’s legitimacy. We propose a variety of statistical measures to quantify the strength of this obfuscation strategy with respect to the structural and semantic characteristics of the reporting attacks in our dataset.

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