A Systematic Review of Cryptographic Indexing Schemes for Encrypted Scientific Databases: Methods, Architectures, and Future Research Directions

Abstract

The rapid growth of encrypted scientific databases has introduced critical challenges in secure data retrieval, efficient indexing, and privacy-preserving query processing. Traditional cryptographic mechanisms, while ensuring confidentiality, often limit searchability and performance, creating a fundamental trade-off between security and usability. This paper presents a systematic review of cryptographic indexing schemes designed for encrypted scientific databases, focusing on methodologies, architectural patterns, and emerging research directions. The study synthesizes findings from thirty peer-reviewed works published between 2018 and 2025, analyzing techniques such as searchable encryption, homomorphic encryption, order-preserving encryption, and chaotic polynomial-based indexing. The review highlights how modern approaches integrate machine learning and generative artificial intelligence to enhance adaptive indexing and threat detection. Key findings reveal that while significant progress has been made in balancing efficiency and security, challenges remain in scalability, leakage resilience, and real-time processing. This paper contributes by providing a comprehensive comparative analysis, identifying research gaps, and proposing future directions for integrating cryptographic indexing within modern software engineering pipelines, particularly in DevSecOps environments.