(Mis-)Informed Consent: Predatory Apps and the Exploitation of Populations with Limited Literacy

TL;DR

This paper investigates how predatory apps exploit low-literacy populations by obfuscating privacy disclosures, leading to scams, and evaluates whether LLM-generated summaries can improve understanding.

Contribution

It provides a detailed analysis of privacy disclosure practices in predatory apps and assesses the effectiveness of LLM tools in enhancing user comprehension.

Findings

85% of low-literacy users misunderstood app permissions

Most predatory apps omit or obscure critical privacy disclosures

LLM-generated summaries can potentially improve consent understanding

Abstract

Among populations with limited literacy in emerging digital markets, the adoption of mobile phones, combined with comprehension barriers and poor cybersecurity hygiene, has created hidden privacy risks. This paper examines how informed consent is often abused by predatory financial applications, leading to financial scams that disproportionately affect users with low literacy. We focus on predatory loan, gambling, and trading apps, analyzing a dataset of 50 Google Play Store apps to measure how many omit or obfuscate critical privacy disclosures. We also evaluate comprehension gaps among users with low literacy via a targeted user study and assess whether Large Language Model (LLM)-generated summaries, translations, and visual cues can improve consent clarity. Our findings show that 85% of study participants did not understand basic app permissions, underscoring the urgent need for stronger regulatory oversight and scalable LLM-driven privacy-literacy tools.