合成数据--匿名土拨鼠日
Synthetic Data -- Anonymisation Groundhog Day
摘要：
合成数据被宣传为隐私保护数据发布的银弹解决方案，解决了传统匿名技术的缺点。其承诺是，从生成模型中提取的合成数据保留了原始数据集的统计特性，但同时提供了针对隐私攻击的完美保护。在这项工作中，我们首次对合成数据发布的隐私收益进行了定量评估，并将其与之前的匿名技术进行了比较。我们对各种最先进的生成模型的评估表明，合成数据要么不能防止推理攻击，要么不能保留数据效用。换句话说，我们的经验表明，与传统的匿名技术相比，合成数据并没有在隐私和实用性之间提供更好的权衡。此外，与传统的匿名化相比，合成数据发布的隐私-效用权衡难以预测。由于无法预测合成数据集将保留哪些信号以及将丢失哪些信息，因此合成数据导致高度可变的隐私增益和不可预测的效用损失。总之，我们发现合成数据远不是保护隐私的数据发布的圣杯。
英文摘要：
Synthetic data has been advertised as a silver-bullet solution to privacy-preserving data publishing that addresses the shortcomings of traditional anonymisation techniques. The promise is that synthetic data drawn from generative models preserves the statistical properties of the original dataset but, at the same time, provides perfect protection against privacy attacks. In this work, we present the first quantitative evaluation of the privacy gain of synthetic data publishing and compare it to that of previous anonymisation techniques. Our evaluation of a wide range of state-of-the-art generative models demonstrates that synthetic data either does not prevent inference attacks or does not retain data utility. In other words, we empirically show that synthetic data does not provide a better tradeoff between privacy and utility than traditional anonymisation techniques. Furthermore, in contrast to traditional anonymisation, the privacy-utility tradeoff of synthetic data publishing is hard to predict. Because it is impossible to predict what signals a synthetic dataset will preserve and what information will be lost, synthetic data leads to a highly variable privacy gain and unpredictable utility loss. In summary, we find that synthetic data is far from the holy grail of privacy-preserving data publishing.
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。
