近年來智慧型手機的崛起，我們利用智慧型手機上所擁有的感測器去感測資訊並提供給所需要的人，此系統我們稱為參與式感測系統。我們提出可以將傳統的機會式感測系統跟參與式感測系統一起運作會達到相輔相成的效果。我們將這兩個感測系統一起運作並稱為行動式感測系統。在參與式系統中，由於參與者是人類，所以在資訊上傳中一定會有隱私議題。我們使用了密碼學中的群簽章方法來解決隱私議題。在傳統機會式感測方面，我們則使用身分式簽章來保護資訊的安全。
在使用群簽章的過程中，我們把原本群簽章的有限體上運算改建置在橢圓曲線上來達到加速的效果，並且將利用地區來分群藉此可以減少參與者與伺服器的溝通次數來減少溝通的延遲。在實作方面，我們實做群簽章在手機上面去並測量實際運算所需要的時間，利用測量出來的時間去模擬參與者在台北市中所需要等在手機運算的平均延遲時間。最後我們將一般人使用群簽章以資料類型來分群的方式和以地區分群的方式做效能的比較，我們的地區分群方法可以有效的提高群簽章效能。

Due to increasing sensing capacity, smartphones offer opportunity to monitor human activity. By combining traditional sensors deployed in fixed positions and sensors embedded in smartphones carried by human, a new paradigm of sensing, named mobile sensing, is introduced. In particular, the mobile users act as queriers who request information provided by the mobile users who act as data collectors, and the data collection and delivery are facilitated by human mobility and ubiquity. However, human factors involving in mobile sensing rises additional privacy considerations. This thesis respectively exploits group and identity-based signatures to preserve privacy of information supported by fixed sensors and smartphones. However, the complicated computation of group signatures mounts workloads on resource-limited smartphones. We therefore group the mobile users according to their current positions rather than the information type they queried. The physical proximity feature of human makes a low frequency of joining and leaving a group in our regional grouping approach, thereby reducing the computation overheads and delay in group signature. We implement group signature on the smartphone and conduct a simulation experiment to investigate the performance of proposed regional grouping approach. The simulation results show that the regional grouping approach is more efficient than traditional one grouped by information type.

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