摘要
连续查询时由于轨迹位置间的相关性,满足差分隐私定义要求的拉普拉斯混淆机制在查询次数较少时可以很好地起到位置隐私保护作用,但独立向每个真实位置添加噪声导致隐私预算水平迅速消耗.针对这一问题,提出一种可预测的差分扰动用户轨迹隐私保护方法,由预测函数、测试函数和噪声机制三部分构成,同时使用预算管理器配置每步所需参数.如果预测函数生成的干扰位置通过测试函数,则直接使用该位置请求服务,否则通过噪声机制重新生成一干扰位置.实验结果表明,该方法比单独向每个位置添加噪声在轨迹偏移度和隐私预算消耗率上均具有较高优势,实现了隐私保护度与服务质量的平衡.
During continuous query,the Laplacian obfuscation mechanism that meets differential privacy definition can play a good role in position privacy protection when the number of queries is small,but the independent noise is added to each real position results in a privacy budget level quickly consumes due to trajectory position correlation. To solve this problem,this paper proposes a predictable differential perturbation user trajectory privacy protection method consisting of a prediction function,a test function and a noise mechanism.At the same time,a budget manager is used to configure the parameters required for each step. If the perturbation position generated by the prediction function passes the test function,it is directly used to request the service,otherwise,an perturbation position is regenerated by the noise mechanism. The experimental results show that this method has higher advantages in track offset degree and privacy budget consumption rate than independently adding noise to each location and achieves a balance between privacy protection and service quality.
引文
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