Collective Obfuscation and Crowdsourcing

TL;DR

This paper investigates obfuscation tactics in crowdsourcing reporting platforms, analyzing real-world data to identify coordinated strategies that threaten platform integrity and proposing measures to quantify their effectiveness.

Contribution

It introduces a threat model and taxonomy for obfuscation in crowdsourcing, along with empirical analysis and statistical measures to assess obfuscation strategies.

Findings

Identified coordinated obfuscation strategies in call logs

Developed statistical measures to quantify obfuscation strength

Highlighted security and privacy implications of reporting platforms

Abstract

Crowdsourcing technologies rely on groups of people to input information that may be critical for decision-making. This work examines 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 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.