Differentially Private Data Generation with Missing Data

• URL: http://arxiv.org/abs/2310.11548v2
• Date: Thu, 30 May 2024 19:38:24 GMT
• Title: Differentially Private Data Generation with Missing Data
• Authors: Shubhankar Mohapatra, Jianqiao Zong, Florian Kerschbaum, Xi He,
• Abstract summary: We formalize the problems of differential privacy (DP) synthetic data with missing values. We propose three effective adaptive strategies that significantly improve the utility of the synthetic data. Overall, this study contributes to a better understanding of the challenges and opportunities for using private synthetic data generation algorithms.
• Score: 25.242190235853595
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite several works that succeed in generating synthetic data with differential privacy (DP) guarantees, they are inadequate for generating high-quality synthetic data when the input data has missing values. In this work, we formalize the problems of DP synthetic data with missing values and propose three effective adaptive strategies that significantly improve the utility of the synthetic data on four real-world datasets with different types and levels of missing data and privacy requirements. We also identify the relationship between privacy impact for the complete ground truth data and incomplete data for these DP synthetic data generation algorithms. We model the missing mechanisms as a sampling process to obtain tighter upper bounds for the privacy guarantees to the ground truth data. Overall, this study contributes to a better understanding of the challenges and opportunities for using private synthetic data generation algorithms in the presence of missing data.

Related papers

• Mitigating the Privacy Issues in Retrieval-Augmented Generation (RAG) via Pure Synthetic Data [51.41288763521186]
  Retrieval-augmented generation (RAG) enhances the outputs of language models by integrating relevant information retrieved from external knowledge sources. RAG systems may face severe privacy risks when retrieving private data. We propose using synthetic data as a privacy-preserving alternative for the retrieval data.
  arXiv  Detail & Related papers  (2024-06-20T22:53:09Z)
• Best Practices and Lessons Learned on Synthetic Data [83.63271573197026]
  The success of AI models relies on the availability of large, diverse, and high-quality datasets. Synthetic data has emerged as a promising solution by generating artificial data that mimics real-world patterns.
  arXiv  Detail & Related papers  (2024-04-11T06:34:17Z)
• Scaling While Privacy Preserving: A Comprehensive Synthetic Tabular Data Generation and Evaluation in Learning Analytics [0.412484724941528]
  Privacy poses a significant obstacle to the progress of learning analytics (LA), presenting challenges like inadequate anonymization and data misuse. Synthetic data emerges as a potential remedy, offering robust privacy protection. Prior LA research on synthetic data lacks thorough evaluation, essential for assessing the delicate balance between privacy and data utility.
  arXiv  Detail & Related papers  (2024-01-12T20:27:55Z)
• Trading Off Scalability, Privacy, and Performance in Data Synthesis [11.698554876505446]
  We introduce (a) the Howso engine, and (b) our proposed random projection based synthetic data generation framework. We show that the synthetic data generated by Howso engine has good privacy and accuracy, which results the best overall score. Our proposed random projection based framework can generate synthetic data with highest accuracy score, and has the fastest scalability.
  arXiv  Detail & Related papers  (2023-12-09T02:04:25Z)
• Reimagining Synthetic Tabular Data Generation through Data-Centric AI: A Comprehensive Benchmark [56.8042116967334]
  Synthetic data serves as an alternative in training machine learning models. ensuring that synthetic data mirrors the complex nuances of real-world data is a challenging task. This paper explores the potential of integrating data-centric AI techniques to guide the synthetic data generation process.
  arXiv  Detail & Related papers  (2023-10-25T20:32:02Z)
• Synthetic data, real errors: how (not) to publish and use synthetic data [86.65594304109567]
  We show how the generative process affects the downstream ML task. We introduce Deep Generative Ensemble (DGE) to approximate the posterior distribution over the generative process model parameters.
  arXiv  Detail & Related papers  (2023-05-16T07:30:29Z)
• Beyond Privacy: Navigating the Opportunities and Challenges of Synthetic Data [91.52783572568214]
  Synthetic data may become a dominant force in the machine learning world, promising a future where datasets can be tailored to individual needs. We discuss which fundamental challenges the community needs to overcome for wider relevance and application of synthetic data.
  arXiv  Detail & Related papers  (2023-04-07T16:38:40Z)
• PreFair: Privately Generating Justifiably Fair Synthetic Data [17.037575948075215]
  PreFair is a system that allows for Differential Privacy (DP) fair synthetic data generation. We adapt the notion of justifiable fairness to fit the synthetic data generation scenario.
  arXiv  Detail & Related papers  (2022-12-20T15:01:54Z)
• 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)
• Enabling Synthetic Data adoption in regulated domains [1.9512796489908306]
  The switch from a Model-Centric to a Data-Centric mindset is putting emphasis on data and its quality rather than algorithms. In particular, the sensitive nature of the information in highly regulated scenarios needs to be accounted for. A clever way to bypass such a conundrum relies on Synthetic Data: data obtained from a generative process, learning the real data properties.
  arXiv  Detail & Related papers  (2022-04-13T10:53:54Z)

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.