Location-based queries enable fundamental services for mobile users. While the benefits of location-based services (LBS) are numerous, exposure of mobile users’ locations to untrusted LBS providers may lead to privacy concerns. This article proposes StarCloak , a utility-aware and attack-resilient location anonymization service for privacy-preserving LBS usage. StarCloak combines several desirable properties. First, unlike conventional approaches which are indifferent to underlying road network structure, StarCloak uses the concept of star s and proposes cloaking graphs for effective location cloaking on road networks. Second, StarCloak supports user-specified $k$ -user anonymity and $l$ -segment indistinguishability, for enabling personalized privacy protection and for serving users with varying privacy preferences. Third, StarCloak achieves strong attack-resilience against replay and query injection attacks through randomized star selection and pruning. Finally, to enable efficient query processing with high throughput and low bandwidth overhead, StarCloak makes cost-aware star selection decisions by considering query evaluation and network communication costs. We evaluate StarCloak on two datasets using real-world road networks, under various privacy and utility constraints. Results show that StarCloak achieves improved query success rate and throughput, reduced anonymization time and network usage, and higher attack-resilience in comparison to XStar , its most relevant competitor.

中文翻译：

---

实用程序感知和隐私保护移动查询服务

基于位置的查询为移动用户提供基本服务。虽然基于位置的服务 (LBS) 的好处很多，但将移动用户的位置暴露给不受信任的 LBS 提供商可能会导致隐私问题。本文提出了StarCloak ，这是一种实用程序感知和抗攻击的位置匿名服务，用于保护隐私的 LBS 使用。StarCloak结合了几个理想的特性。首先，与对底层道路网络结构无动于衷的传统方法不同，StarCloak使用星的概念 并提出了用于道路网络上有效位置隐藏的隐藏图。二、StarCloak支持用户指定$k$ -用户匿名和$l$ -分段不可区分性，用于实现个性化隐私保护并为具有不同隐私偏好的用户提供服务。第三，StarCloak通过随机星选择和修剪实现了对重放和查询注入攻击的强大攻击弹性。最后，为了实现高吞吐量和低带宽开销的高效查询处理，StarCloak通过考虑查询评估和网络通信成本做出具有成本意识的星选择决策。我们在各种隐私和效用约束下，使用真实世界的道路网络在两个数据集上评估StarCloak 。结果表明StarCloak 与其最相关的竞争对手XStar相比，提高了查询成功率和吞吐量，减少了匿名化时间和网络使用，并提高了抗攻击能力。