论文标题
部分可观测时空混沌系统的无模型预测
A Neural Approach to Spatio-Temporal Data Release with User-Level Differential Privacy
论文作者
论文摘要
几家公司（例如Meta，Google）已经启动了“数据对”项目，在这些项目中，总体位置数据首先被公开消毒和公开发布，这对许多在运输，公共卫生，公共卫生（例如Covid-19-19s vrail）和Urban Planning方面的应用程序很有用。差异隐私（DP）是选择的保护模型，以确保生成原始位置数据的个人的隐私。但是，当每个人贡献多个位置报告（即在用户级隐私下）时，当前的解决方案无法保留数据实用程序。为了抵消此限制，Meta和Google的公众发布使用高隐私预算（例如$ε$ = 10-100），导致隐私不良。我们提出了一种新颖的方法来私下，准确地释放时空数据。我们采用神经网络的模式识别能力，特别是变异自动编码器（VAE），以减少DP机制引入的噪声，从而提高了准确性，同时仍然满足隐私要求。我们对实际数据集进行的广泛的实验评估表明，与基准相比，我们的方法具有明显的优势。
Several companies (e.g., Meta, Google) have initiated "data-for-good" projects where aggregate location data are first sanitized and released publicly, which is useful to many applications in transportation, public health (e.g., COVID-19 spread) and urban planning. Differential privacy (DP) is the protection model of choice to ensure the privacy of the individuals who generated the raw location data. However, current solutions fail to preserve data utility when each individual contributes multiple location reports (i.e., under user-level privacy). To offset this limitation, public releases by Meta and Google use high privacy budgets (e.g., $ε$=10-100), resulting in poor privacy. We propose a novel approach to release spatio-temporal data privately and accurately. We employ the pattern recognition power of neural networks, specifically variational auto-encoders (VAE), to reduce the noise introduced by DP mechanisms such that accuracy is increased, while the privacy requirement is still satisfied. Our extensive experimental evaluation on real datasets shows the clear superiority of our approach compared to benchmarks.
