Acquiring traffic condition information is of great significance in transportation guidance, urban planning, and route recommendation. To date, traffic density data are generally acquired by road sound analysis, video data analysis, or in-vehicle network communication, which are usually financially or temporally expensive. Another way to get traffic conditions is to collect track data by crowdsourcing. However, this way lead to a greater risk of leaking users’ privacy. To avoid the risk, this article proposes a traffic density estimation model based on crowdsourcing privacy protection. First, in the acquisition process of the track data by crowdsourcing, dual servers are employed for transmission, and homomorphic encryption is carried out to encrypt the data to protect the data from being leaked during transmission. Second, sampling is implemented for randomization and anonymization to reduce the spatial continuity and temporal continuity of position data. In this way, the intermediate server cannot acquire users’ original data, and the main server cannot obtain users’ personal information. Finally, before data transmission, Laplace noising is performed on the users’ local position data to further protect the original location information. The proposed algorithm in this study realizes that only users have their original track data, and the servers involved in the work cannot infer the original track data, which ensures the real security of user privacy. The proposed algorithm was verified with the track data from the Didi Gaia Data Opening Plan. The experimental results showed that the proposed algorithm could still maintain the validity of data analysis results and the security of user data privacy after homomorphic encryption, noise addition, and sample collection, and displayed good robustness and scalability.

中文翻译：

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一种基于众包隐私保护的流量密度估计模型

获取交通状况信息对于交通引导、城市规划和路线推荐具有重要意义。迄今为止，交通密度数据通常通过道路声音分析、视频数据分析或车载网络通信来获取，这些通常在经济上或时间上都是昂贵的。另一种获取交通状况的方法是通过众包收集跟踪数据。但是，这种方式导致泄露用户隐私的风险更大。为规避风险，本文提出一种基于众包隐私保护的流量密度估计模型。首先，在众包获取轨迹数据的过程中，采用双服务器进行传输，并通过同态加密对数据进行加密，保护数据在传输过程中不被泄​​露。第二，采样实现随机化和匿名化，以降低位置数据的空间连续性和时间连续性。这样，中间服务器就无法获取用户的原始数据，主服务器也无法获取用户的个人信息。最后，在数据传输之前，对用户的本地位置数据进行拉普拉斯噪声处理，进一步保护原始位置信息。本研究提出的算法实现了只有用户拥有自己的原始轨迹数据，参与工作的服务器无法推断出原始轨迹数据，保证了用户隐私的真正安全。所提出的算法通过滴滴盖亚数据开放计划中的轨迹数据进行了验证。