Recommender systems have become essential tools for addressing information overload in the digital age. However, the collection and usage of user data for personalized recommendations raise significant privacy concerns. This research focuses on enhancing privacy in recommender systems through the application of differential privacy techniques, particularly in the domain of privacy-preserving data publishing. Our study aims to address three key research questions: (1) developing standardized metrics to characterize and compare recommendation datasets in the context of privacy-preserving data publishing, (2) designing differential privacy algorithms for private data publishing that preserve recommendation quality, and (3) examining the impact of differential privacy on beyond-accuracy objectives in recommender systems. We propose to develop domain-specific metrics for evaluating the similarity between recommendation datasets, analogous to those used in other domains such as trajectory data publication. Additionally, we will investigate methods to balance the trade-off between privacy guarantees and recommendation accuracy, considering the potential disparate impacts on different user subgroups. Finally, we aim to assess the broader implications of implementing differential privacy on beyond-accuracy objectives such as diversity, popularity bias, and fairness. By addressing these challenges, our research seeks to contribute to the advancement of privacy-preserving techniques in recommender systems, facilitating the responsible and secure use of recommendation data while maintaining the utility of personalized suggestions. The outcomes of this study have the potential to significantly benefit the field by enabling the reuse of existing algorithms with minimal adjustments while ensuring robust privacy guarantees.