合成数据：打开数据闸门，使机器学习方法的开发更快、更有针对性
Synthetic Data: Opening the data floodgates to enable faster, more directed development of machine learning methods
摘要：
机器学习的许多突破性进展都可以归因于大量丰富数据的可用性。不幸的是，许多大规模数据集是高度敏感的，例如医疗保健数据，并且不能广泛用于机器学习社区。生成具有隐私保证的合成数据提供了这样一种解决方案，允许“大规模”进行有意义的研究-通过允许整个机器学习社区潜在地加速给定领域的进展。在本文中，我们提供了合成数据的高级视图：它意味着什么，我们如何评估它以及我们如何使用它。
英文摘要：
Many ground-breaking advancements in machine learning can be attributed to the availability of a large volume of rich data. Unfortunately, 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, allowing meaningful research to be carried out "at scale" - by allowing the entirety of the machine learning community to potentially accelerate progress within a given field. In this article, we provide a high-level view of synthetic data: what it means, how we might evaluate it and how we might use it.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
