# Releasing Differentially Private Event Logs Using Generative Models

arXiv: https://arxiv.org/abs/2504.06418 · HTML: https://mlsanity.com/paper/2504.06418/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 37 references that are in the map.
- First posted on arXiv 8 Apr 2025.

## Builds on (most cited first, 26 of 26)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Diffusion Models in Vision: A Survey](https://mlsanity.com/paper/2209.04747/) (2022)
- [Renyi Differential Privacy](https://mlsanity.com/paper/1702.07476/) (2017)
- [TabDDPM: Modelling Tabular Data with Diffusion Models](https://mlsanity.com/paper/2209.15421/) (2022)
- [A General Approach to Adding Differential Privacy to Iterative Training Procedures](https://mlsanity.com/paper/1812.06210/) (2018)
- [Grid Search, Random Search, Genetic Algorithm: A Big Comparison for NAS](https://mlsanity.com/paper/1912.06059/) (2019)
- [Privacy Loss in Apple's Implementation of Differential Privacy on MacOS 10.12](https://mlsanity.com/paper/1709.02753/) (2017)
- [Differentially Private Mixture of Generative Neural Networks](https://mlsanity.com/paper/1709.04514/) (2017)
- [Differentially Private Data Generative Models](https://mlsanity.com/paper/1812.02274/) (2018)
- [Differentially Private Generative Adversarial Networks for Time Series, Continuous, and Discrete Open Data](https://mlsanity.com/paper/1901.02477/) (2019)
- [Predictive Business Process Monitoring via Generative Adversarial Nets: The Case of Next Event Prediction](https://mlsanity.com/paper/2003.11268/) (2020)
- [Towards Formalizing the GDPR's Notion of Singling Out](https://mlsanity.com/paper/1904.06009/) (2019)
- [Group-Based Privacy Preservation Techniques for Process Mining](https://mlsanity.com/paper/2105.11983/) (2021)
- [PRIPEL: Privacy-Preserving Event Log Publishing Including Contextual Information](https://mlsanity.com/paper/2006.12856/) (2020)
- [A Deep Learning Approach for Repairing Missing Activity Labels in Event Logs for Process Mining](https://mlsanity.com/paper/2202.10326/) (2022)
- [Towards Quantifying Privacy in Process Mining](https://mlsanity.com/paper/2012.12031/) (2020)
- [Mine Me but Don't Single Me Out: Differentially Private Event Logs for Process Mining](https://mlsanity.com/paper/2103.11739/) (2021)
- [Generating Privacy-Preserving Process Data with Deep Generative Models](https://mlsanity.com/paper/2203.07949/) (2022)
- [Libra: High-Utility Anonymization of Event Logs for Process Mining via Subsampling](https://mlsanity.com/paper/2206.13050/) (2022)
- [TraVaG: Differentially Private Trace Variant Generation Using GANs](https://mlsanity.com/paper/2303.16704/) (2023)
- [SaCoFa: Semantics-aware Control-flow Anonymization for Process Mining](https://mlsanity.com/paper/2109.08501/) (2021)
- [TraVaS: Differentially Private Trace Variant Selection for Process Mining](https://mlsanity.com/paper/2210.14951/) (2022)

## Abstract (arXiv)

In recent years, the industry has been witnessing an extended usage of process mining and automated event data analysis. Consequently, there is a rising significance in addressing privacy apprehensions related to the inclusion of sensitive and private information within event data utilized by process mining algorithms. State-of-the-art research mainly focuses on providing quantifiable privacy guarantees, e.g., via differential privacy, for trace variants that are used by the main process mining techniques, e.g., process discovery. However, privacy preservation techniques designed for the release of trace variants are still insufficient to meet all the demands of industry-scale utilization. Moreover, ensuring privacy guarantees in situations characterized by a high occurrence of infrequent trace variants remains a challenging endeavor. In this paper, we introduce two novel approaches for releasing differentially private trace variants based on trained generative models. With TraVaG, we leverage textitGenerative Adversarial Networks (GANs) to sample from a privatized implicit variant distribution. Our second method employs textitDenoising Diffusion Probabilistic Models that reconstruct artificial trace variants from noise via trained Markov chains. Both methods offer industry-scale benefits and elevate the degree of privacy assurances, particularly in scenarios featuring a substantial prevalence of infrequent variants. Also, they overcome the shortcomings of conventional privacy preservation techniques, such as bounding the length of variants and introducing fake variants. Experimental results on real-life event data demonstrate that our approaches surpass state-of-the-art techniques in terms of privacy guarantees and utility preservation.