Visualizing Privacy-Relevant Data Flows in Android Applications

2026-07-23 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Software Development & Engineering, Cybersecurity & Data Privacy · Depth: Intermediate, extended

Summary

SliceViz is a new web tool designed to help Android app developers understand and comply with data protection regulations like GDPR and the upcoming EU Cyber Resilience Act (CRA). Developed by researchers at Paderborn University, SliceViz employs static program slicing and software visualization to analyze privacy-relevant data flows within an app's source code. It identifies data sources, categorizes them by risk (risk 1 for identifiable data, risk 2 for data needing additional information), and assigns a six-tier warning level (A-F) based on processing operations. The tool visualizes these program slices in both Jimple and a more readable Java view, with the latter being preferred by users. A user study with 12 participants showed SliceViz effectively aids in identifying privacy-relevant properties, processing apps in an average of 41.96 seconds. The tool achieved a mean System Usability Scale (SUS) score of 63.96, improving to 68 for experienced Java and Android developers.

Key takeaway

For Android app developers navigating GDPR and CRA compliance, SliceViz offers a critical advantage. You can use this tool to automatically identify and visualize privacy-relevant data flows, reducing manual effort in completing data safety forms and DPIAs. Prioritize addressing high-risk data sources using the tool's warning scale and leverage the Java view for clearer code interpretation. This proactive approach helps ensure accurate privacy reporting and mitigates potential legal penalties.

Key insights

Static program slicing and visualization can effectively reveal privacy-relevant data flows in Android apps for compliance.

Principles

• Program slicing identifies privacy-relevant data flows.
• Visualizing code slices enhances developer understanding.
• Risk-based classification prioritizes data protection actions.

Method

SliceViz analyzes Android APKs by statically slicing privacy-relevant data sources using an identifier dataset and library list. It then visualizes these slices in Jimple or Java, applying risk ratings and a warning scale.

In practice

• Use SliceViz to identify high-risk data flows.
• Prioritize privacy actions using the warning scale.
• Leverage Java view for clearer code understanding.

Topics

• Android App Security
• Static Program Slicing
• GDPR Compliance
• Data Flow Visualization
• Privacy by Design
• Cyber Resilience Act

Code references

• wala/WALA
• secure-software-engineering/DroidBench

Best for: CTO, VP of Engineering/Data, Executive, Software Engineer, AI Security Engineer, Legal Professional

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.