LDPTrace：本地差分隐私轨迹合成
LDPTrace: Locally Differentially Private Trajectory Synthesis
AI总结：
LDPTrace提出一种本地差分隐私轨迹合成框架，通过利用用户轨迹的三个关键模式并以低计算成本生成高相似度轨迹，同时提出隐私安全的网格粒度选择方法，实验验证其有效性与高效性。
摘要：
轨迹数据有潜力极大地惠及广泛的实际应用，例如通过人们的移动模式追踪疾病传播，以及基于旅行偏好提供个性化的基于位置的服务。然而，隐私问题和数据保护法规限制了该数据的共享和利用程度。为了克服这一挑战，本地差分隐私提供了一种解决方案，允许人们共享其数据的扰动版本，确保隐私，因为只有数据所有者才能访问原始信息。尽管其潜力巨大，现有的基于点的扰动机制由于效用低下、依赖外部知识、高计算开销以及易受攻击，并不适用于现实场景。为了解决这些局限性，我们引入了LDPTrace，一种新颖的本地差分隐私轨迹合成框架。我们的框架考虑了在本地设置中从用户轨迹推断出的三个关键模式，使我们能够以最小的计算成本合成与真实轨迹高度相似的轨迹。此外，我们提出了一种新方法，在不损害隐私的情况下选择合适的网格粒度。我们使用真实数据、各种效用度量和攻击进行的广泛实验，证明了LDPTrace的有效性和高效性。
英文摘要：
Trajectory data has the potential to greatly benefit a wide-range of real-world applications, such as tracking the spread of the disease through people's movement patterns and providing personalized location-based services based on travel preference. However, privay concerns and data protection regulations have limited the extent to which this data is shared and utilized. To overcome this challenge, local differential privacy provides a solution by allowing people to share a perturbed version of their data, ensuring privacy as only the data owners have access to the original information. Despite its potential, existing point-based perturbation mechanisms are not suitable for real-world scenarios due to poor utility, dependence on external knowledge, high computational overhead, and vulnerability to attacks. To address these limitations, we introduce LDPTrace, a novel locally differentially private trajectory synthesis framework. Our framework takes into account three crucial patterns inferred from users' trajectories in the local setting, allowing us to synthesize trajectories that closely resemble real ones with minimal computational cost. Additionally, we present a new method for selecting a proper grid granularity without compromising privacy. Our extensive experiments using real-world data, various utility metrics and attacks, demonstrate the efficacy and efficiency of LDPTrace.
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。
