A privacy scoring framework: Automation of privacy compliance and risk evaluation with standard indicators

• DOI: 10.1016/j.jksuci.2022.12.019
• Publication Date: 2023-01-03T00:12:17Z
• Authors: Nakyoung Kim; Hyeontaek Oh; Jun Kyun Choi
• Keywords: Privacy Protection; Privacy software

Personal data have become the key to data-driven services and applications whereas privacy requirements are now strongly imposed by regulations. Meanwhile, people find it difficult understand whether handle personal comply with their agreements Therefore, need for indicators, which summarize contents as forms of scoring, labels, etc., has increased empower users' rights providing understandable information about privacy. For firm proper criteria methods evaluating level risks compliance required. Accordingly, this paper proposes a scoring framework in context handling data, inspired six standardized indicators. This introduces detailed on indicators quantify scores. Also, policy based set machine learning-based hierarchical binary classifiers processes quantifying from privacy-related information. Through analyzing policies access lists more than 10,000 mobile Google Play Store investigating case studies some applications, shows feasibility proposed framework.