With the rapid development of data acquisition technology, data acquisition departments can collect increasingly more data. Various data from government agencies are gradually becoming available to the public, including license plate recognition (VLPR) data. As a result, privacy protection is becoming increasingly significant. In this paper, an adversary model based on passing time, color, type, and brand of VLPR data is proposed. Through experimental analysis, the tracking probability of a vehicle’s trajectory can be more than 94% if utilizing the original data. To decrease the tracking probability, a novel approach called the (m, n)-bucket model based on time series is proposed since previous works, such as those using generalization and bucketization models, cannot deal with data with multiple sensitive attributes (SAs) or data with time correlations. Meanwhile, a mathematical model is established to expound the privacy protection principle of the (m, n)-bucket model. By comparing the average calculated linking probability of all individuals and the actual linking probability, it is shown that the mathematical model that is proposed can well expound the privacy protection principle of the (m, n)-bucket model. Extensive experiments confirm that our technique can effectively prevent trajectory privacy disclosures.

中文翻译：

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基于VLPR数据的车辆轨迹隐私保护方法

随着数据采集技术的飞速发展，数据采集部门可以收集越来越多的数据。来自政府机构的各种数据正逐渐向公众公开，包括车牌识别（VLPR）数据。结果，隐私保护变得越来越重要。本文提出了一种基于时间，颜色，类型和品牌的VLPR数据的对手模型。通过实验分析，如果利用原始数据，车辆轨迹的跟踪概率可以超过94％。为了降低跟踪概率，一种称为（m，n提出了基于时间序列的存储桶模型，因为先前的工作（例如使用泛化和存储桶化模型的工作）无法处理具有多个敏感属性（SA）的数据或具有时间相关性的数据。同时，建立了数学模型来阐述（m，n）桶模型的隐私保护原理。通过比较所有个体的平均计算链接概率和实际链接概率，表明所提出的数学模型可以很好地阐述（m，n）-bucket模型的隐私保护原理。大量实验证实，我们的技术可以有效地防止轨迹隐私泄露。