Recommender Systems (RSs) have gained widespread popularity for providing personalized recommendations in manifold domains. However, considering the growing user privacy concerns, the development of recommender systems that prioritize data protection has become increasingly important. In indoor environments, RSs face unique challenges, and ongoing research is being conducted to address them. Anonymous Session-Based Recommender Systems (ASBRSs) can represent a possible solution to address these challenges while ensuring user privacy. This paper aims to bridge the gap between existing RS research and the demand for privacy-preserving recommender systems, especially in indoor settings, where significant research efforts are underway. Therefore, it proposes three research questions: How does user modeling based on implicit feedback impact on ASBRSs, considering different embedding extraction networks? How can short sessions be leveraged to start the recommendation process in ASBRSs? To what extent can ASBRSs generate fair recommendations? By investigating these questions, this study establishes the foundations for applying ASBRSs in indoor environments, safeguarding user privacy, and contributing to the ongoing research in this field.