DiffAudit: Auditing Privacy Practices of Online Services for Children and Adolescents
Olivia Figueira, Rahmadi Trimananda, Athina Markopoulou, Scott Jordan

TL;DR
DiffAudit is a novel platform-agnostic methodology that uses differential analysis and GPT-4 based data classification to audit privacy practices of online services for children, adolescents, and adults, revealing concerning data handling behaviors.
Contribution
The paper introduces DiffAudit, a new privacy auditing framework combining differential network traffic analysis with GPT-4 data classification, enabling detailed comparison of data practices across user ages and consent states.
Findings
Detected problematic data practices before consent and age disclosure
Identified lack of differentiation in age-specific data flows
Revealed sharing of linkable data with third parties
Abstract
Children's and adolescents' online data privacy are regulated by laws such as the Children's Online Privacy Protection Act (COPPA) and the California Consumer Privacy Act (CCPA). Online services that are directed towards general audiences (i.e., including children, adolescents, and adults) must comply with these laws. In this paper, first, we present DiffAudit, a platform-agnostic privacy auditing methodology for general audience services. DiffAudit performs differential analysis of network traffic data flows to compare data processing practices (i) between child, adolescent, and adult users and (ii) before and after consent is given and user age is disclosed. We also present a data type classification method that utilizes GPT-4 and our data type ontology based on COPPA and CCPA, allowing us to identify considerably more data types than prior work. Second, we apply DiffAudit to a set of…
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Taxonomy
TopicsPrivacy, Security, and Data Protection · Child Development and Digital Technology
