在現今世界裡，資料蒐集是一種不斷在進行的行為，也因此個人隱私也越來越重要。因為像是健康或是人口統計的研究，我們會需要公開資料，也因此部分明顯可以辨識身分的屬性需要被移除，像是姓名、病歷號碼。但資料蒐集會不斷進行，因此仍有可能會被其他的資料裡的屬性，像是郵遞區號、性別、年齡重新辨識出受害者，而像這樣的方式我們稱之為連結攻擊。
而防止受害者被重新辨識的方法，稱為De-identification。k-anonymity即為一個有效防止連結攻擊的方法之一。並且利用generalization或suppression來確保每一個受害者都無法從k個裡辨識出來。在此論文中，我們會探討什麼是k-anonymity，並且再依Samarati提出的minimal generalization的定義來找出minimal generalization。我們會介紹由X. SUN 提出的Hash-based algorithm，並且提出一個不僅能改善效能還可以增加彈性的新的演算法來找出minimal generalization以達到k-anonymity。

Abstract
Nowadays, data collection is an ongoing process, so personal privacy becomes more and more important. We need to publish data for purposes such as public health and demographic research. And some attributes which can clearly be used to identify individuals (such as name and medical record ID) are generally removed. Nevertheless, the database that people can sometimes join with other public databases on attributes (such as ZIP code, gender as well as age) to re-identify individuals can be called the “linking" attack.
The way to protect individuals from re-identification is called “de-identification”. k-anonymity is an efficient way to prevent the linking attack by generalization or suppression so that no individual can be uniquely distinguished from a group of size k. In this thesis, we will discuss what k-anonymity is and find the minimal generalization in the sense of [13] , described by Samarati. We will introduce the hash-based algorithm proposed by X. SUN, and propose a much more efficient as well as flexible algorithm that can be used to find the minimal generalization achieving k-anonymity.

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