GDPR-Bench-Android: A Benchmark for Evaluating Automated GDPR Compliance Detection in Android
Authors
Huaijin Ran
Haoyi Zhang
Xunzhu Tang
Abstract
Automating the detection of EU General Data Protection Regulation (GDPR) violations in source code is a critical but underexplored challenge. We introduce \textbf{GDPR-Bench-Android}, the first comprehensive benchmark for evaluating diverse automated methods for GDPR compliance detection in Android applications. It contains \textbf{1951} manually annotated violation instances from \textbf{15} open-source repositories, covering 23 GDPR articles at file-, module-, and line-level granularities. To enable a multi-paradigm evaluation, we contribute \textbf{Formal-AST}, a novel, source-code-native formal method that serves as a deterministic baseline. We define two tasks: (1) \emph{multi-granularity violation localization}, evaluated via Accuracy@\textit{k}; and (2) \emph{snippet-level multi-label classification}, assessed by macro-F1 and other classification metrics. We benchmark 11 methods, including eight state-of-the-art LLMs, our Formal-AST analyzer, a retrieval-augmented (RAG) method, and an agentic (ReAct) method. Our findings reveal that no single paradigm excels across all tasks. For Task 1, the ReAct agent achieves the highest file-level Accuracy@1 (17.38%), while the Qwen2.5-72B LLM leads at the line level (61.60%), in stark contrast to the Formal-AST method's 1.86%. For the difficult multi-label Task 2, the Claude-Sonnet-4.5 LLM achieves the best Macro-F1 (5.75%), while the RAG method yields the highest Macro-Precision (7.10%). These results highlight the task-dependent strengths of different automated approaches and underscore the value of our benchmark in diagnosing their capabilities. All resources are available at: https://github.com/Haoyi-Zhang/GDPR-Bench-Android.
