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Advancing Android Privacy Assessments with Automation

Mugdha Khedkar, Michael Schlichtig, Eric Bodden

TL;DR

The paper addresses the fragmentation in Android privacy assessments under GDPR and the upcoming CRA by proposing an automated workflow, the Assessor View, to bridge the gap between developers, DPOs, and legal experts. It details a two-module architecture: Privacy Slice Visualizer for static code slicing of privacy-related data flows, and Assessor View to map those slices to DPV-based representations across three abstraction levels for different stakeholders. Key contributions include integrating the Data Privacy Vocabulary (DPV) into code-level analysis, creating multi-view representations, and validating the concept through a case-study-inspired exploration that highlights GDPR implications and data minimization opportunities. The work emphasizes collaboration with DPV maintainers and DPOs, and discusses challenges such as scalability, usability, and maintaining alignment with evolving privacy standards, aiming to enable faster, more accurate, and cost-effective privacy assessments in Android ecosystems.

Abstract

Android apps collecting data from users must comply with legal frameworks to ensure data protection. This requirement has become even more important since the implementation of the General Data Protection Regulation (GDPR) by the European Union in 2018. Moreover, with the proposed Cyber Resilience Act on the horizon, stakeholders will soon need to assess software against even more stringent security and privacy standards. Effective privacy assessments require collaboration among groups with diverse expertise to function effectively as a cohesive unit. This paper motivates the need for an automated approach that enhances understanding of data protection in Android apps and improves communication between the various parties involved in privacy assessments. We propose the Assessor View, a tool designed to bridge the knowledge gap between these parties, facilitating more effective privacy assessments of Android applications.

Advancing Android Privacy Assessments with Automation

TL;DR

The paper addresses the fragmentation in Android privacy assessments under GDPR and the upcoming CRA by proposing an automated workflow, the Assessor View, to bridge the gap between developers, DPOs, and legal experts. It details a two-module architecture: Privacy Slice Visualizer for static code slicing of privacy-related data flows, and Assessor View to map those slices to DPV-based representations across three abstraction levels for different stakeholders. Key contributions include integrating the Data Privacy Vocabulary (DPV) into code-level analysis, creating multi-view representations, and validating the concept through a case-study-inspired exploration that highlights GDPR implications and data minimization opportunities. The work emphasizes collaboration with DPV maintainers and DPOs, and discusses challenges such as scalability, usability, and maintaining alignment with evolving privacy standards, aiming to enable faster, more accurate, and cost-effective privacy assessments in Android ecosystems.

Abstract

Android apps collecting data from users must comply with legal frameworks to ensure data protection. This requirement has become even more important since the implementation of the General Data Protection Regulation (GDPR) by the European Union in 2018. Moreover, with the proposed Cyber Resilience Act on the horizon, stakeholders will soon need to assess software against even more stringent security and privacy standards. Effective privacy assessments require collaboration among groups with diverse expertise to function effectively as a cohesive unit. This paper motivates the need for an automated approach that enhances understanding of data protection in Android apps and improves communication between the various parties involved in privacy assessments. We propose the Assessor View, a tool designed to bridge the knowledge gap between these parties, facilitating more effective privacy assessments of Android applications.
Paper Structure (6 sections, 4 figures, 1 table)

This paper contains 6 sections, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Problem
  3. Approach
  4. Case Study
  5. Challenges and Future Plans
  6. Conclusion

Figures (4)

  • Figure 1: Our approach. Contributions of Module 1 are under submission.
  • Figure 2: View 1 (Java slice) for Roidsec
  • Figure 3: View 2 (DPV model) for Roidsec; ex = example, dpv = data privacy vocabulary, pd = personal data
  • Figure 4: View 3 (DPV diagram) for Roidsec