Research on Secure Data Aggregation Techniques in Wireless Sensor Networks (WSNs) focuses on efficiently collecting and combining sensor data while preserving confidentiality, integrity, and authenticity in the presence of malicious attacks. Since traditional aggregation reduces communication cost but risks data tampering or leakage, recent approaches integrate cryptographic primitives, homomorphic encryption, and lightweight authentication schemes to ensure secure in-network processing. Techniques such as privacy-preserving aggregation, trust-based models, and blockchain-assisted frameworks are being adopted to detect compromised nodes and prevent false data injection. Additionally, machine learning and optimization-based algorithms enhance anomaly detection and adaptively balance security with energy efficiency. Overall, secure data aggregation remains a vital research direction to achieve reliable, privacy-aware, and energy-efficient data transmission in modern WSN applications.