在交通估计的参与式传感方案中保护代理隐私
Preserving Privacy of Agents in Participatory-Sensing Schemes for Traffic Estimation
AI总结：
本文提出了一种衡量代理在交通网络中隐私侵犯的指标，并设计算法平衡隐私保护与估计误差，探讨了隐私敏感节点广播策略对隐私保护的影响。
摘要：
本文介绍了一种衡量代理在参与式传感方案中穿越交通网络时隐私侵犯程度的指标。该指标定义为外部观察者根据随时间广播的所有位置测量值，对交通网络中的私有节点（如家庭或办公室位置）所赋予的条件概率。提出了一种算法，用于在隐私侵犯程度和预期估计误差之间找到最优平衡，该误差由参与者停止广播位置的节点数量捕捉。算法搜索了一类策略，其中代理在距离隐私敏感节点（以跳数计）小于预设阈值时停止传输位置测量。采用此类对称策略在实现资源和计算简便性方面具有优势。结果扩展到更一般化的策略。进一步探讨了人口密度异质性对最优策略的影响。最后，数值研究了中介度中心性与代理最优隐私保护策略之间的关系。
英文摘要：
A measure of privacy infringement for agents (or participants) travelling across a transportation network in participatory-sensing schemes for traffic estimation is introduced. The measure is defined to be the conditional probability that an external observer assigns to the private nodes in the transportation network, e.g., location of home or office, given all the position measurements that it broadcasts over time. An algorithm for finding an optimal trade-off between the measure of privacy infringement and the expected estimation error, captured by the number of the nodes over which the participant stops broadcasting its position, is proposed. The algorithm searches over a family of policies in which an agent stops transmitting its position measurements if its distance (in terms of the number of hops) to the privacy sensitive node is smaller than a prescribed threshold. Employing such symmetric policies are advantageous in terms of the resources required for implementation and the ease of computation. The results are expanded to more general policies. Further, the effect of the heterogeneity of the population density on the optimal policy is explored. Finally, the relationship between the betweenness measure of centrality and the optimal privacy-preserving policy of the agents is numerically explored.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
