<p>Pervasive computing environments are designed to operate efficiently in diverse contexts, delivering personalized recommendations at any time, anywhere, and for any purpose. However, because they handle sensitive personal data, privacy becomes a critical concern. With personalized recommendation systems becoming integral to daily life, achieving a balance between robust privacy protection and system effectiveness presents substantial challenges. This survey offers a comprehensive analysis of privacy-preserving methodologies developed for pervasive recommender systems (PRS). We rigorously review the recent state of research, identifying key privacy challenges inherent in recommendation frameworks, particularly those arising from malicious attacks such as data breaches, unauthorized access, and adversarial manipulations. We examine existing techniques and solutions designed to mitigate these threats, such as encryption, anonymization, federated learning, blockchain, and differential privacy, evaluating their strengths and limitations in pervasive environments. Additionally, we discuss how improving context awareness through emerging technologies such as edge/fog computing, blockchain, and federated learning can provide promising pathways toward decentralized, distributed computing models. Finally, we outline future research directions that aim to develop robust and scalable solutions that protect user privacy without compromising the performance of recommendation algorithms.</p>