Smart parking in vehicular networks is an emerging technology that has been proposed to alleviate congestion in urban areas by employing intelligent systems, secure communication, and real-time decision-making. Smart parking systems enable vehicles to interact seamlessly with the infrastructure, thereby ensuring user convenience with minimal fuel consumption and waiting times. However, ensuring secure and privacy-preserving operation is a major challenge, and this is particularly true when dealing with threats such as unauthorized access and replay attacks. Various solutions such as blockchain-based smart parking systems and privacy-preserving cryptographic protocols have been proposed to overcome these challenges. However, these solutions are associated with high computational overhead and latency. In this paper, we propose an SLQ (Secure Lightweight and Queue Balanced)-based smart parking system that employs Elliptic Curve Cryptography (ECC) for lightweight key generation, Advanced Encryption Standard in Galois/Counter Mode (AES-GCM) for secure and authenticated encryption, and JSON Web Tokens for session validation. A challenge-response mechanism is employed to ensure the authenticity of the vehicle prior to accessing the parking facility, and replay protection strategies are also incorporated to prevent misuse of stale requests. We implement our framework using six candidate methods and compare our results against three well-known baselines (DyPARK, PriParkRec, and PrivRep) under identical Simulation of Urban Mobility (SUMO) settings. Experimental results show that our SLQ-based parking method achieves SPEI = 0.837 and throughput = 811.170 veh/h, exceeding the nearest competitor, DyPARK (SPEI = 0.834, throughput = 809.870 veh/h), while reducing the registration delay from 0.596 ms to 0.240 ms (59.7% improvement).