Comprehensive data-driven systems require the integration of various access control and privacy patterns to address the diverse needs of subjects. However, existing approaches often struggle to simultaneously support precise access control, privacy preservation, and efficient policy maintenance. This paper presents G2D (Graph to Data), a novel technique that employs a Data Regulatory Graph (DRG) to dynamically generate data authorization statements tailored to specific subjects. G2D unifies access control and privacy by producing authorized SQL queries and specifying necessary data transformations for sensitive fields. Experimental results demonstrate that G2D incurs minimal execution overhead, simplifies policy updates, and effectively balances system performance with data protection, even under high concurrency. These findings highlight G2D’s potential to support scalable, privacy-aware data access in complex environments.

Developing front-end applications involving large datasets with concurrent usage poses significant challenges. These challenges encompass efficient data retrieval and visualization. This paper addresses these issues within the domain of time-series data and explores methods such as progressive data retrieval and data aggregation strategies to effectively manage and present large datasets.

This paper explores the user experience (UX) de-sign challenges for individuals with varying levels of digital proficiency, focusing on the TanRabad SURVEY app as a case study. Through the analysis of data evidence and user interviews, common obstacles are identified, and potential solutions to address these challenges are proposed. These solutions have been evaluated by approximately 30 users and have shown effectiveness in resolving the identified issues.