對社會網路而言，使用者對使用者之間的隱私揭露會有不同程度的意願，然而目前的社會網路並沒有提供使用者一個良好的環境去撰寫屬於自己的隱私揭露規則。W3C對於Web Privacy的部份提出了P3P這個解決方案。此研究利用W3C所發佈的P3P與APPEL做為基礎平台讓使用者去撰寫屬於自己的隱私揭露規則，以控制使用者的隱私資料；而由於P3P與APPEL的設計並不具有語意，所以加入Semantic Web使其不會產生語意模糊不清的情況。而為了能讓使用者快速的瞭解一個人之可信任程度，本研究以Google的Page Rank演算法進行修改，設計一名為個人信任指數 (Personal Trust Rank，PTR)找出此平台中任一人之可信任的指數，以此去評估一個人是否是可信任狀態；最後找出兩個人之間的隱私揭露是否產生衝突，同時讓使用者可利用PTR設立門檻以達到Access Control之能力，據此以達成保護個人隱私之目標。

In the social network, people have different privacy disclosure intention. However, it is not an easy way for a user to declare his/her own privacy disclosure policy, we adopt the P3P/APPEL languages to solve this problem. A user`s privacy policy is shown as APPEL and a server’s privacy declaration statement is expressed as P3P. However, P3P and APPEL are XML-based so they might have semantic inconsistency and ambiguity in the privacy policy representations. We resolve these issues by using the semantic web technologies to enable the privacy policy enforcement. To decide whether a person is trustworthy, we modify the Google`s Page Rank algorithm for Personal Trust Rank ( PTR ) to evaluate a person’s trust. Finally a reasoning engine is used to find out if there are any privacy disclosure inconsistencies between users.

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