近年來空間查詢的隱私保護議題越來越受到重視，不管是一次性查詢或連續性查詢，都對隱私權保護有所要求。而這其中，又以連續性查詢牽扯到連續相關與移動性，要達到隱私保護上有其相當的困難點，故較少有論文針對此方向進行研究，另外大部分的隱私保護研究都著重在隨機移動模組(random way point model)，而非實際情況下，會受網路或道路限制的移動模組(network constrained model)。本論文在network constrained model環境下提出了一個新的路徑隱私保護(path privacy)做為位置隱私保護(location privacy)在連續性查詢下的延伸，並對此提出一種新型態的隱私權需求M-cut，希望連續性的CSR內，都可以利用M條路徑，混淆攻擊者，令其無法將某條特定路段與查詢者做直接對應，猜測出查詢會經過某條路段。本論文最後提出兩個建構CSR的演算法，random way 與junction sharing，前者在建構CSR著重於隨機處理，後者可以在滿足隱私需求後有效降低伺服器端的資料運算量。

In recent years, the research of privacy protection for spatial query is getting more and more important. No matter snap shot query or continuous query, both of them have request on privacy protection. Since the privacy protection under continuous query has its own difficulty to breakthrough recently, few studies are proposed in this certain area. Besides, most of studies are not focused on the real situation as network constrained model, but rather on random way point model. This thesis proposes a new path privacy based on network constrained model environment as the extension of location privacy under continuous query, and also presents a new type of privacy requirement, named M-cut. We hope every CSR can manipulate these m paths to confuse adversaries to prevent a segment or a route from being linked to a specific user, or inferring some segment that the user will pass through. This thesis proposes two methods to create CSR, one is so-called random way, another is junction sharing. The former focus on random selection on CSR creation, the latter can cut down the transporting cost of the server.

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