Providing a provable privacy guarantees while maintaining the utility of data is a challenging task in many real-world applications. Recently, a new framework called One-Sided Differential Privacy (OSDP) was introduced that extends existing differential privacy approaches. OSDP increases the utility of the data by taking advantage of the fact that not all records are sensitive. However, the previous work assumed that all records are statistically independent from each other. Motivated by occupancy data in building management systems, this paper extends the existing one-sided differential privacy framework. In this paper, we quantify the overall privacy leakage when the adversary is given dependency information between the records. In addition, we show how an optimization problem can be constructed that efficiently trades off between the utility and privacy.
翻译:在许多现实应用中,在维护数据效用的同时提供可变隐私保障是一项挑战性任务。最近,推出了一个名为“单面不同隐私”的新框架,扩展了现有的差异隐私办法。OSDP利用并非所有记录都敏感这一事实,提高了数据的效用。然而,先前的工作假设所有记录在统计上都是独立的。本文以建筑管理系统中的占用数据为动力,扩展了现有的单面差异隐私框架。本文将对手在记录之间获得依赖性信息时的隐私总体渗漏量化。此外,我们展示了如何构建优化问题,从而有效地将效用与隐私进行交换。