面向路网环境速度预测攻击的隐私保护

发布时间：2018-03-30 13:04

本文选题：轨迹隐私　切入点：隐私保护　出处：《西安交通大学学报》2017年02期

【摘要】：针对路网环境中攻击者利用速度预测获得用户位置隐私的问题,提出了一种提高当前路段查询密度值的密度压缩算法。该算法在用户真实位置附近添加大量噪声用户,通过噪声用户影响当前路段查询密度,进而降低速度预测的准确性,破坏攻击者通过概率转移矩阵预测用户行驶速度的攻击行为,以此保护用户在路网环境中的位置轨迹隐私。该算法通过密度压缩使真实用户和噪声用户表现出相同速度,提高了真实用户与噪声用户之间的相似程度,降低了噪声用户被识别的机率,进一步隐藏了真实用户。实验结果表明,与其他主流算法相比,密度压缩算法能够更有效地抵抗基于速度预测的攻击行为,具有更好的隐私保护能力。在执行时间和隐私保护成功率等方面的实验结果进一步表明,该算法更适合在路网环境下提供隐私保护服务,具有广阔的应用前景。
[Abstract]:In order to solve the problem of obtaining user location privacy by speed prediction in road network environment, a density compression algorithm is proposed to improve the density value of the current section query, which adds a large number of noisy users near the real location of the user. The noise user affects the query density of the current section, which reduces the accuracy of the speed prediction, and destroys the attack behavior of the attacker to predict the user's driving speed through the probability transfer matrix. In order to protect the privacy of the user's location path in the road network environment, the algorithm makes the real user and the noise user show the same speed through density compression, and improves the similarity degree between the real user and the noise user. The experimental results show that the density compression algorithm can resist the attack behavior based on speed prediction more effectively than other popular algorithms. The experimental results on the execution time and the success rate of privacy protection further show that the proposed algorithm is more suitable for providing privacy protection services in the network environment and has a broad application prospect.
【作者单位】：哈尔滨工程大学计算机科学与技术学院;佳木斯大学信息电子技术学院;
【基金】：国家自然科学基金资助项目(61472097) 高等学校博士学科点专项科研基金资助项目(20132304110017) 黑龙江省自然科学基金资助项目(F2015022)
【分类号】：TP309

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