论文标题
小屋：在差异隐私保护下为车辆互联网启用高级，批处理查询
HUT: Enabling High-UTility, Batched Queries under Differential Privacy Protection for Internet-of-Vehicles
论文作者
论文摘要
汽车互联网（IOV）的新兴趋势要求集中的服务器收集/处理敏感数据，而单个车辆上的计算资源有限。这种敏感数据的中心化需要实际的隐私保护。一个广泛应用的范式，差异隐私可以通过添加噪声来对敏感数据提供强大的保证。但是，直接将DP应用于IOV会对数据实用性和有效保护面临重大挑战。我们观察到，关于IOV中支持DP的保护的关键问题在于如何协同将DP与IOV的特殊特征结合在一起，IOV的特殊特征通常是由于集中式服务器与边缘车辆之间的频繁相互作用而形成不平衡的批次。 为此，我们提出了小屋，这是一种新算法，可以在IOV中提供高实用程序以支持DP的保护。我们的主要见解是利用IOV：不平衡批次中的固有特征。我们的关键想法是汇总本地批次并应用订单约束，以便可以减轻DP保护中的信息丢失。我们评估了小屋针对最先进的DP保护机制的有效性。结果表明，小屋可以提供95.69％\％的信息丢失，同时可以对敏感数据进行强大的数学保证保护。
The emerging trends of Internet-of-Vehicles (IoV) demand centralized servers to collect/process sensitive data with limited computational resources on a single vehicle. Such centralizations of sensitive data demand practical privacy protections. One widely-applied paradigm, Differential Privacy, can provide strong guarantees over sensitive data by adding noises. However, directly applying DP for IoV incurs significant challenges for data utility and effective protection. We observe that the key issue about DP-enabled protection in IoV lies in how to synergistically combine DP with special characteristics of IoV, whose query sequences are usually formed as unbalanced batches due to frequent interactions between centralized servers and edge vehicles. To this end, we propose HUT, a new algorithm to enable High UTility for DP-enabled protection in IoV. Our key insight is to leverage the inherent characteristics in IoV: the unbalanced batches. Our key idea is to aggregate local batches and apply Order Constraints, so that information loss from DP protection can be mitigated. We evaluate the effectiveness of HUT against the state-of-the-art DP protection mechanisms. The results show that HUT can provide much lower information loss by 95.69\% and simultaneously enable strong mathematically-guaranteed protection over sensitive data.
