TransRisk: Mobility Privacy Risk Prediction based on Transferred Knowledge
TransRisk: Mobility Privacy Risk Prediction based on Transferred Knowledge
复制标题
TransRisk：基于转移知识的移动隐私风险预测
DOI：
10.1145/3534581
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Zhang, Desheng
中科院分区：
文献类型：
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作者：
Xie, Xiaoyang;Hong, Zhiqing;Qin, Zhou;Fang, Zhihan;Tian, Yuan;Zhang, Desheng
人类移动数据可能会导致隐私问题，因为即使使用匿名用户id，恶意攻击也可以通过这些数据重新识别居民。对于收集移动数据的城市服务，有效的隐私风险评估对于保护用户隐私至关重要。现有的方法能够有效地进行隐私风险评估，服务运营商可以利用预测模型快速调整感知数据的质量以降低隐私风险。然而，对于这些预测模型，大多数都需要大量的训练数据，这些训练数据必须首先被收集和存储。如此大规模的长期训练数据收集，与城市新型服务隐私风险预测的目的相矛盾，该目的是确保在短时间内将高风险的人类出行数据的质量调整到低隐私风险。为了解决这一问题，我们提出了一种基于迁移学习的隐私风险预测模型TransRisk，通过(1)自身的小规模短期数据和(2)从其他现有城市服务数据中学习的知识来预测新的目标城市服务的隐私风险。我们设想TransRisk在交通摄像头监控系统上的应用，并使用已经在中国城市深圳收集的真实交通数据集进行评估，包括四个源数据集，即(i)一个呼叫详细记录数据集（CDR），拥有120万用户；（ii）一个120万用户的手机连接数据集（CONN）；（iii） 1万辆车辆的车载GPS数据集（车辆）；（iv）拥有15.6万用户的电子收费交易数据集（ETC），以及一个目标数据集，即拥有248个摄像头的摄像头数据集（camera）。结果表明，我们的模型在RMSE和MAE方面优于最先进的方法。我们的工作还为当前和未来大规模服务的移动数据隐私风险评估提供了有价值的见解和启示。
Human mobility data may lead to privacy concerns because a resident can be re-identified from these data by malicious attacks even with anonymized user IDs. For an urban service collecting mobility data, an efficient privacy risk assessment is essential for the privacy protection of its users. The existing methods enable efficient privacy risk assessments for service operators to fast adjust the quality of sensing data to lower privacy risk by using prediction models. However, for these prediction models, most of them require massive training data, which has to be collected and stored first. Such a large-scale long-term training data collection contradicts the purpose of privacy risk prediction for new urban services, which is to ensure that the quality of high-risk human mobility data is adjusted to low privacy risk within a short time. To solve this problem, we present a privacy risk prediction model based on transfer learning, i.e., TransRisk, to predict the privacy risk for a new target urban service through (1) small-scale short-term data of its own, and (2) the knowledge learned from data from other existing urban services. We envision the application of TransRisk on the traffic camera surveillance system and evaluate it with real-world mobility datasets already collected in a Chinese city, Shenzhen, including four source datasets, i.e., (i) one call detail record dataset (CDR) with 1.2 million users; (ii) one cellphone connection data dataset (CONN) with 1.2 million users; (iii) a vehicular GPS dataset (Vehicles) with 10 thousand vehicles; (iv) an electronic toll collection transaction dataset (ETC) with 156 thousand users, and a target dataset, i.e., a camera dataset (Camera) with 248 cameras. The results show that our model outperforms the state-of-the-art methods in terms of RMSE and MAE. Our work also provides valuable insights and implications on mobility data privacy risk assessment for both current and future large-scale services.