In the era of smart cities, Internet of Things, and Mobility-as-a-Service, private operators need to share data with public agencies to support data exchanges for “living lab” ecosystems more than ever before. However, it is still problematic for private operators to share data with the public due to risks to competitive advantages. A privacy control algorithm is proposed to overcome this key obstacle for private operators sharing complex network-oriented data objects. The algorithm is based on information-theoretic k-anonymity and, using tour data as an example, where an operator’s data is used in conjunction with performance measure accuracy controls to synthesize a set of alternative tours with diffused probabilities for sampling during a query. The algorithm is proven to converge sublinearly toward a constrained maximum entropy under certain asymptotic conditions with measurable gap. Computational experiments verify the applicability to multi-vehicle fleet tour data; they confirm that reverse engineered parameters from the diffused data result in controllable sampling error; and tests conducted on a set of realistic routing records from travel data in Long Island, NY, demonstrate the use of the methodology from both the adversary and user perspectives.

在智慧城市，物联网和移动即服务时代，私人运营商需要与公共机构共享数据，以支持“生活实验室”生态系统的数据交换。然而，由于竞争优势的风险，私人运营商与公众共享数据仍然存在问题。针对私有运营商共享复杂的面向网络的数据对象的问题，提出了一种隐私控制算法。该算法基于信息理论的k匿名性，并以旅行数据为例，其中，操作员的数据与性能度量准确性控件结合使用，以合成一组具有分散概率的备选旅行，以在查询期间进行采样。证明该算法在某些具有可测量间隙的渐近条件下朝着约束最大熵亚线性收敛。计算实验验证了多车队旅行数据的适用性；他们确认从分散数据中反向工程的参数会导致可控的采样误差；对来自纽约长岛的旅行数据进行的一系列现实路线记录的测试，从对手​​和用户的角度证明了该方法的使用。