Beyond Privacy: Navigating the Opportunities and Challenges of Synthetic Data

• URL: http://arxiv.org/abs/2304.03722v1
• Date: Fri, 7 Apr 2023 16:38:40 GMT
• Title: Beyond Privacy: Navigating the Opportunities and Challenges of Synthetic Data
• Authors: Boris van Breugel and Mihaela van der Schaar
• Abstract summary: 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.
• Score: 91.52783572568214
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generating synthetic data through generative models is gaining interest in the ML community and beyond. In the past, synthetic data was often regarded as a means to private data release, but a surge of recent papers explore how its potential reaches much further than this -- from creating more fair data to data augmentation, and from simulation to text generated by ChatGPT. In this perspective we explore whether, and how, synthetic data may become a dominant force in the machine learning world, promising a future where datasets can be tailored to individual needs. Just as importantly, we discuss which fundamental challenges the community needs to overcome for wider relevance and application of synthetic data -- the most important of which is quantifying how much we can trust any finding or prediction drawn from synthetic data.

Related papers

• Exploring the Landscape for Generative Sequence Models for Specialized Data Synthesis [0.0]
  This paper introduces a novel approach that leverages three generative models of varying complexity to synthesize Malicious Network Traffic. Our approach transforms numerical data into text, re-framing data generation as a language modeling task. Our method surpasses state-of-the-art generative models in producing high-fidelity synthetic data.
  arXiv  Detail & Related papers  (2024-11-04T09:51:10Z)
• 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)
• MALLM-GAN: Multi-Agent Large Language Model as Generative Adversarial Network for Synthesizing Tabular Data [10.217822818544475]
  We propose a framework to generate synthetic (tabular) data powered by large language models (LLMs) Our approach significantly enhance the quality of synthetic data generation in common scenarios with small sample sizes. Our results demonstrate that our model outperforms several state-of-art models regarding generating higher quality synthetic data for downstream tasks while keeping privacy of the real data.
  arXiv  Detail & Related papers  (2024-06-15T06:26:17Z)
• 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)
• 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)
• Exploring the Potential of AI-Generated Synthetic Datasets: A Case Study on Telematics Data with ChatGPT [0.0]
  This research delves into the construction and utilization of synthetic datasets, specifically within the telematics sphere, leveraging OpenAI's powerful language model, ChatGPT. To illustrate this data creation process, a hands-on case study is conducted, focusing on the generation of a synthetic telematics dataset.
  arXiv  Detail & Related papers  (2023-06-23T15:15:13Z)
• 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)
• Machine Learning for Synthetic Data Generation: A Review [23.073056971997715]
  This paper reviews existing studies that employ machine learning models for the purpose of generating synthetic data. The review encompasses various perspectives, starting with the applications of synthetic data generation, spanning computer vision, speech, natural language processing, healthcare, and business domains. The paper also addresses the crucial aspects of privacy and fairness concerns related to synthetic data generation.
  arXiv  Detail & Related papers  (2023-02-08T13:59:31Z)
• Synthetic Data: Opening the data floodgates to enable faster, more directed development of machine learning methods [96.92041573661407]
  Many ground-breaking advancements in machine learning can be attributed to the availability of a large volume of rich data. Many large-scale datasets are highly sensitive, such as healthcare data, and are not widely available to the machine learning community. Generating synthetic data with privacy guarantees provides one such solution.
  arXiv  Detail & Related papers  (2020-12-08T17:26:10Z)

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.