Edge computing has become a fundamental paradigm in real-time data processing of latency-sensitive applications like smart healthcare, Internet of Things (IoT), and financial systems. Nevertheless, current edge and cloud-based solutions do not provide a high level of privacy or have a high level of computational and communication overhead because of intensive cryptographic actions. In this paper, a lightweight privacy-conscious edge computing architecture is proposed and can be used to provide secure and low-latency computation on sensitive data based on a combination of feature-level selective lightweight homomorphic encryption, context-sensitive differential privacy, and fully edge-enforced privacy control. In contrast to traditional solutions, which use consistent encryption or fixed privacy controls, the proposed system only encrypts privacy-sensitive data characteristics and dynamically adjusts differential privacy noise depending on the actual system state. The design utilizes significantly fewer resources and offers superior scalability without compromising privacy guarantees. Extensive simulations in a realistic edge computing environment show that the proposed architecture can reduce the encryption overhead by up to 60 percent, reduce the communication latency to approximately 2 ms, and maintain an accuracy of over 98 percent under strict privacy requirements. The comparison of results indicates that the suggested framework is more efficient in computing, scalable, and compliant with regulations than cloud-based and current edge computing solutions. The suggested solution offers a realistic and scalable answer to real-time privacy-preserving edge analytics within resource-constrained environments.