Liu Zhenpeng, Miao Dewei, Li Ruilin, Liu Yi, Li Xiaofei
Information Technology Center, Hebei University, Baoding 071002, China.
School of Cyber Security and Computer, Hebei University, Baoding 071002, China.
Entropy (Basel). 2023 Jan 19;25(2):201. doi: 10.3390/e25020201.
Users who initiate continuous location queries are prone to trajectory information leakage, and the obtained query information is not effectively utilized. To address these problems, we propose a continuous location query protection scheme based on caching and an adaptive variable-order Markov model. When a user initiates a query request, we first query the cache information to obtain the required data. When the local cache cannot satisfy the user's demand, we use a variable-order Markov model to predict the user's future query location and generate a -anonymous set based on the predicted location and cache contribution. We perturb the location set using differential privacy, then send the perturbed location set to the location service provider to obtain the service. We cache the query results returned by the service provider to the local device and update the local cache results according to time. By comparing the experiment with other schemes, the proposed scheme in this paper reduces the number of interactions with location providers, improves the local cache hit rate, and effectively ensures the security of the users' location privacy.
发起连续位置查询的用户容易出现轨迹信息泄露的情况,并且所获取的查询信息没有得到有效利用。为了解决这些问题,我们提出了一种基于缓存和自适应变阶马尔可夫模型的连续位置查询保护方案。当用户发起查询请求时,我们首先查询缓存信息以获取所需数据。当本地缓存无法满足用户需求时,我们使用变阶马尔可夫模型预测用户未来的查询位置,并基于预测位置和缓存贡献生成一个 -匿名集。我们使用差分隐私对位置集进行扰动,然后将扰动后的位置集发送给位置服务提供商以获取服务。我们将服务提供商返回的查询结果缓存到本地设备,并根据时间更新本地缓存结果。通过与其他方案进行实验比较,本文提出的方案减少了与位置提供商的交互次数,提高了本地缓存命中率,并有效地确保了用户位置隐私的安全性。