Abstract

Privacy-preserving techniques protect user data in mobile apps, where heavy data collection elevates risk. This systematic literature review examines techniques used in mobile software engineering, their adoption across development approaches, and their effectiveness. Common methods include encryption, Privacy by Design, differential privacy, blockchain-based schemes, on-device learning, and privacy-preserving reputation models. They mitigate unauthorized access, data breaches, and inference attacks, but uptake is limited by computational overhead, privacy–utility trade-offs, and scalability. A key gap is the lack of a unified evaluation framework, hindering cross-study comparisons. Domain needs are different healthcare stresses regulatory compliance and confidentiality; finance emphasizes secure transactions; IoT prioritizes lightweight, edge-friendly controls. Future work should standardize metrics, refine hybrid models, and improve usability without reducing protection at scale.