The rapid growth of Internet of things (IoT) networks requires the effective and secure data aggregation systems that would maintain the confidentiality and provide real-time analytics. The present paper introduces an algebraic cryptographic approach to the problem of secure IoT data aggregation, basing it on structured mathematical primitives to guarantee computation on distributed sensor data upholding privacy. The framework combines algebraic encoding, homomorphic transformation and lightweight encryption schemes to facilitate aggregation in a secure manner without revealing raw data. A formalized mathematical model is built that models the aggregation operations in modular algebraic spaces which are computationally efficient and adversarial resilient. The recommended algorithms are measured in terms of the level of security, the accuracy of aggregation, and the rate of communication showing enhancements in efficiency and lower latency than traditional algorithms. Findings show improved scalability, resiliency and applicability in resource limited IoT settings, which make the framework viable in actual deployments.