Releasing Differentially Private Event Logs Using Generative Models

• URL: http://arxiv.org/abs/2504.06418v1
• Date: Tue, 08 Apr 2025 20:35:53 GMT
• Title: Releasing Differentially Private Event Logs Using Generative Models
• Authors: Frederik Wangelik, Majid Rafiei, Mahsa Pourbafrani, Wil M. P. van der Aalst,
• Abstract summary: We introduce two novel approaches for releasing private trace variants based on trained generative models.<n>Our approaches surpass state-of-the-art techniques in terms of privacy guarantees and utility preservation.
• Score: 0.13124513975412253
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 \textit{Generative Adversarial Networks} (GANs) to sample from a privatized implicit variant distribution. Our second method employs \textit{Denoising 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.

Related papers

• Enhancing Privacy-Utility Trade-offs to Mitigate Memorization in Diffusion Models [62.979954692036685]
  We introduce PRSS, which refines the classifier-free guidance approach in diffusion models by integrating prompt re-anchoring and semantic prompt search. Our approach consistently improves the privacy-utility trade-off, establishing a new state-of-the-art.
  arXiv  Detail & Related papers  (2025-04-25T02:51:23Z)
• Federated Learning with Differential Privacy: An Utility-Enhanced Approach [12.614480013684759]
  Federated learning has emerged as an attractive approach to protect data privacy by eliminating the need for sharing clients' data.<n>Recent studies have shown that federated learning alone does not guarantee privacy, as private data may still be inferred from the uploaded parameters to the central server.<n>We present a modification to these vanilla differentially private algorithms based on a Haar wavelet transformation step and a novel noise injection scheme that significantly lowers the bound of the noise variance.
  arXiv  Detail & Related papers  (2025-03-27T04:48:29Z)
• PersGuard: Preventing Malicious Personalization via Backdoor Attacks on Pre-trained Text-to-Image Diffusion Models [51.458089902581456]
  We introduce PersGuard, a novel backdoor-based approach that prevents malicious personalization of specific images. Our method significantly outperforms existing techniques, offering a more robust solution for privacy and copyright protection.
  arXiv  Detail & Related papers  (2025-02-22T09:47:55Z)
• Differentially Private Random Feature Model [52.468511541184895]
  We produce a differentially private random feature model for privacy-preserving kernel machines.<n>We show that our method preserves privacy and derive a generalization error bound for the method.
  arXiv  Detail & Related papers  (2024-12-06T05:31:08Z)
• Differentially Private Adaptation of Diffusion Models via Noisy Aggregated Embeddings [23.687702204151872]
  We introduce a novel method for adapting diffusion models under differential privacy constraints.<n>This method enables privacy-preserving style and content transfer without fine-tuning model weights.<n> Experimental results show that the TI-based adaptation achieves superior fidelity in style transfer, even under strong privacy guarantees.
  arXiv  Detail & Related papers  (2024-11-22T00:09:49Z)
• Activity Recognition on Avatar-Anonymized Datasets with Masked Differential Privacy [64.32494202656801]
  Privacy-preserving computer vision is an important emerging problem in machine learning and artificial intelligence.<n>We present anonymization pipeline that replaces sensitive human subjects in video datasets with synthetic avatars within context.<n>We also proposeMaskDP to protect non-anonymized but privacy sensitive background information.
  arXiv  Detail & Related papers  (2024-10-22T15:22:53Z)
• A Unified View of Differentially Private Deep Generative Modeling [60.72161965018005]
  Data with privacy concerns comes with stringent regulations that frequently prohibited data access and data sharing. Overcoming these obstacles is key for technological progress in many real-world application scenarios that involve privacy sensitive data. Differentially private (DP) data publishing provides a compelling solution, where only a sanitized form of the data is publicly released.
  arXiv  Detail & Related papers  (2023-09-27T14:38:16Z)
• TraVaG: Differentially Private Trace Variant Generation Using GANs [0.4014524824655105]
  TraVaG is a new approach for releasing differentially private trace variants based on textGenerative Adversarial Networks (GANs) TraVaG overcomes shortcomings of conventional privacy preservation techniques such as bounding the length of variants and introducing fake variants.
  arXiv  Detail & Related papers  (2023-03-29T13:54:32Z)
• Breaking the Communication-Privacy-Accuracy Tradeoff with $f$-Differential Privacy [51.11280118806893]
  We consider a federated data analytics problem in which a server coordinates the collaborative data analysis of multiple users with privacy concerns and limited communication capability. We study the local differential privacy guarantees of discrete-valued mechanisms with finite output space through the lens of $f$-differential privacy (DP) More specifically, we advance the existing literature by deriving tight $f$-DP guarantees for a variety of discrete-valued mechanisms.
  arXiv  Detail & Related papers  (2023-02-19T16:58:53Z)
• Differentially-Private Data Synthetisation for Efficient Re-Identification Risk Control [3.8811062755861956]
  $epsilon$-PrivateSMOTE is a technique for safeguarding against re-identification and linkage attacks. Our proposal combines synthetic data generation via noise-induced adversarial with differential privacy principles to obfuscate high-risk cases.
  arXiv  Detail & Related papers  (2022-12-01T13:20:37Z)
• Private Set Generation with Discriminative Information [63.851085173614]
  Differentially private data generation is a promising solution to the data privacy challenge. Existing private generative models are struggling with the utility of synthetic samples. We introduce a simple yet effective method that greatly improves the sample utility of state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-11-07T10:02:55Z)
• PEARL: Data Synthesis via Private Embeddings and Adversarial Reconstruction Learning [1.8692254863855962]
  We propose a new framework of data using deep generative models in a differentially private manner. Within our framework, sensitive data are sanitized with rigorous privacy guarantees in a one-shot fashion. Our proposal has theoretical guarantees of performance, and empirical evaluations on multiple datasets show that our approach outperforms other methods at reasonable levels of privacy.
  arXiv  Detail & Related papers  (2021-06-08T18:00:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.