動態社群網路之身分匿名

Abstract

隨著社群網站的蓬勃發展，社群網路資料被廣泛的應用於各式各樣不同的需求當中，但是公開的社群網路資料往往伴隨著個人資料隱私的問題，也讓該議題成為使用者和開發人員無不關心的地方。時下關於社群網路隱私保護的研究多數著重在靜態的社群網路上，然而資訊量的增加使得連續發佈資料的必要性大增，在這種動態的社群網路中，僅僅利用先前靜態的保護措施會產生許多隱私洩漏的疑慮。在這篇論文裡，我們先指出個人的身分和社群資訊在動態社群網路中存在被識別出的風險，然後再提出避免此風險的方法。我們闡述了一個新的隱私保護模型：k^w-structural diversity anonymity，在這裡 w 表示攻擊者在動態社群網路中觀察受害者的時間。此隱私保護模型將舊有的 k-structural diversity anonymity 延伸到動態的情境之下。我們提出了一個演算法將發布的動態社群網路加密以符合這個新的隱私保護模型。利用真實社群網路資料和人工社群網路資料來評價這個演算法，結果顯示此方法能在確保隱私保護的條件之下保留很大程度的原始社群網路特徵。

Privacy in publishing social network data is always an important concern. Nowadays most prior privacy protection techniques focus on static social networks. However, there are additional privacy disclosures in dynamic social networks due to the sequential publications. In this thesis, we first show that the risks of vertex or community re-identification exist in a dynamic social network, even if the network published at each time instance is protected by a static anonymity scheme. To prevent vertex and community re-identification in a dynamic social network, we develop novel dynamic k^w-structural diversity anonymity, where w is the time that an adversary can monitor a victim. This scheme extends the k-structural diversity anonymity to a dynamic scenario. We present a heuristic method to anonymize the networks to satisfy the proposed privacy scheme. The evaluations on both real and synthetic data sets show that our approach can retain much of the characteristic of the networks while confirming the privacy protection.