<p>The increasing reliance on data-driven technologies such as Artificial Intelligence and Process Mining has transformed various sectors. Yet, access to real-world data is often restricted by privacy concerns. Synthetic data offers a promising solution by enabling secure data sharing while preserving key characteristics for analysis. This paper introduces the Private Autoregressive Log Synthesizer (PALSYN), a novel approach for generating synthetic event logs with differential privacy guarantees. It employs advanced deep learning techniques to capture the complexity of event data while ensuring privacy. In contrast to existing methods, PALSYN can synthesize private multi-perspective event logs. The evaluation demonstrates the approach’s ability to generate synthetic event logs that closely resemble the original data across key metrics. However, the results highlight the inherent privacy-utility trade-off, with stricter privacy settings introducing noise that strongly impacts utility. By enabling the generation of synthetic event logs with formal privacy guarantees, PALSYN demonstrates significant potential for securely sharing event data in privacy-sensitive domains.</p>