Design and Implementation of JD Tech Mobile App Privacy Compliance Detection System

Background With the rapid growth of mobile applications and increasing concerns over personal data protection, enterprises face regulatory pressure and lack effective tools to assess privacy compliance of their apps.

Industry Pain Points Key issues include lack of awareness of privacy risks during development, reliance on manual source code review, difficulty detecting third‑party SDK risks, and insufficient mechanisms for runtime data protection.

Design Philosophy The proposed system combines static and dynamic scanning, privacy policy analysis, self‑assessment tools, comprehensive reporting, and collaborative management to provide end‑to‑end privacy risk detection for mobile apps.

Core Capabilities 1. Scanning Engine: supports APK/SDK static analysis (decompression, decompilation, rule matching) and dynamic analysis (runtime monitoring, traffic capture). 2. Client‑side Dynamic Hook: injects a custom client to capture app behavior and network data. 3. Operations Platform: manages uploads, displays multi‑dimensional results, and generates remediation reports. 4. Multi‑App Multi‑Team Collaboration: role‑based access for development, security, and compliance teams.

System Architecture The system consists of a Web Front‑End (Vue), BFF layer (Node+Koa), Node Server (business, data, infrastructure), and Detect Engine (static/dynamic scanners). Network topology shows Front‑End, BFF, Node Server, and Detect Engine as core nodes.

Detection Process Static scanning extracts manifest, resources, certificates, and decompiled code to assess permission usage and SDK integration. Dynamic scanning captures runtime traffic, UI interactions, and API call stacks. Privacy policy analysis checks for required disclosures. Results are aggregated into detailed reports.

Implementation Details Static analysis includes APK unpacking, manifest parsing, resource analysis, certificate inspection, decompilation (jadx, baksmali), tracker detection, and rule matching (regex/string). Dynamic analysis uses Xposed hooking, custom client for screenshots and data capture, and automated scenario execution via cloud‑controlled devices.

Conclusion The system addresses the lack of effective privacy compliance tools by providing comprehensive static and dynamic analysis, policy checking, and collaborative workflows, thereby helping app operators and compliance teams identify and remediate privacy risks efficiently.