Event logs capture the execution of processes, record activities and additional information. A trace represents a single instance of a process and includes a sequence of activity records and case attributes with additional information. Event logs may contain sensitive personal information that could harm an individual’s privacy if it is published without pre-processing. Differential privacy (DP) limits the disclosure of new information about any individual when publishing an event log beyond the publicly available background knowledge. Many privacy-preserving approaches to event log publishing ensure DP. Traditional methods focus on preserving the control flow but omit case attributes, limiting comprehensive process analysis based on these attributes. This work addresses this limitation by proposing a novel privacy-preserving event log publishing framework. Our approach ensures privacy for the control flow and case attributes, utilising synthetic tabular data generation approaches based on machine learning that guarantee DP. The framework allows for the use of various tabular data generation approaches. Experimental results with real-world event data demonstrate the framework’s feasibility and highlight the trade-off between data utility and the guaranteed levels of privacy.