現今，社會網絡在世界中越來越流行並受到注視。社會網絡中包含許多個體之間的互動資訊，而這些資訊中可能包含具隱私性的內容。因此，為了保護社會網路中的隱私性資訊，許多相關的匿名化方法已經被提出。在之前的研究中，只注重開發針對單一社會網絡中的隱私性資訊進行防護並發布單一個匿名化後的圖形的匿名化方法。不過，提供單一個匿名化後的圖形資料，可能不足以分析整個社會網絡的演進。所以，在這邊論文我們要解決如何對一時間序列社會網絡資料中的隱私性互動關係進行防護，並發布這段時間序列中多個不同時間點的匿名化後的社會網絡資料。我們指出利用現存的匿名化方法去分別產生並發布不同時間點的匿名化後的社會網絡資料，可能會發生隱私性資訊遭到惡意攻擊者透過觀察發布的多筆不同時間點匿名化後的社會網絡資料而被輕易揭露。因此，我們提供了一個匿名方法，考慮如何對一時間序列的社會網絡資料，同時發布這段時間內多個不同時間點的匿名化後的社會網絡，並保障其隱私性的內容不會輕易的被揭露。我們在論文中詳細介紹我們的實驗步驟，並透過回答一系列的聚合查詢來評估這些匿名化後的社會網絡資料的實用性。實驗結果顯示，透過我們設計的匿名化方法所產生的多個不同時間點的匿名化後的社會網路資料，用來回答查詢能有準確的查詢結果。

Nowadays, social networks have gained popularity among the world. Social networks have a lot of data about interaction among entities and these data may contain the individual privacy content. Accordingly, many studies have been proposed to protect the privacy in social networks. The previous works only focus on developing the privacy preserving methods for releasing a single anonymized graph used to represent a social network. However, the single anonymized graph may not be enough for analyzing the evolution of the whole network. Therefore, we address a novel problem of preserving the privacy of interaction among entities for multiple releases on time-series social network data in this thesis, which means we will release multiple anonymized graphs to represent a social network with time-series data. We point out that the privacy may be revealed across the multiple releases, if we apply the existing methods to generate the individual anonymized graph for the network in the different timestamps. We provide an anonymization method for releasing multiple anonymized graphs at one time on time-series social network data. We detail our experiment steps and evaluate the utility of the anonymized graphs by answering a series of aggregate queries. The results show that the multiple releases generated by our method answer the queries accurately.

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