Optimizing Data Integrity: State-of-the-Art Privacy and Security Techniques in Database Management

This research focuses on optimizing data integrity, security, and privacy in Database Management Systems (DBMS). With the growing volume and complexity of data in various industries, maintaining the accuracy, consistency, and security of databases is crucial. This study explores state-of-the-art techniques for enhancing data integrity, including transaction control, concurrency management, and error detection. Additionally, privacy-centric security frameworks, such as privacy-by-design principles, pseudonymization, and secure multi-party computation, are examined for their effectiveness in ensuring compliance with data protection regulations like GDPR and HIPAA. The research also addresses common security threats such as SQL injection, ransomware, and DDoS attacks, offering practical mitigation techniques. The findings suggest that a holistic approach combining data integrity optimization, robust security measures, and privacy protections is essential for building resilient DBMS that meet both performance and regulatory requirements. Future advancements in cryptography, quantum computing, and blockchain are identified as promising areas for further enhancing DBMS security and privacy.