Data release has privacy disclosure risk if not taking any protection policy. Although attributes that clearly identify individuals, such as Name, Identity Number, are generally removed or decrypted, attackers can still link these databases with other released database on attributes (Quasi-identifiers) to re-identify individual’s private information. K-anonymity is a significant method for priv...

Data release has privacy disclosure risk if not taking any protection policy. Although attributes that clearly identify individuals, such as Name, Identity Number, are generally removed or decrypted, attackers can still link these databases with other released database on attributes (Quasi-identifiers) to re-identify individual’s private information. K-anonymity is a significant method for priv...

p-Sensitive k-anonymity is a sophistication of k-anonymity requiring that there be at least p different values for each confidential attribute within the records sharing a combination of key attributes. Like for k-anonymity, the computational approach originally proposed to achieve this property is based on generalization and suppression; this has several data utility problems, such as turning ...

The widespread deployment of technologies with tracking capabilities, like GPS, GSM, RFID and on-line social networks, allows mass collection of spatio-temporal data about their users. As a consequence, several methods aimed at anonymizing spatio-temporal data before their publication have been proposed in recent years. Such methods are based on a number of underlying privacy models. Among thes...

We formally study two methods for data sanitation that have been used extensively in the database community: k-anonymity and l-diversity. We settle several open problems concerning the difficulty of applying these methods optimally, proving both positive and negative results: – 2-anonymity is in P. – The problem of partitioning the edges of a triangle-free graph into 4-stars (degree-three verti...

Individual privacy will be at risk if a published data set is not properly de-identified. k-anonymity is a major technique to de-identify a data set. A more general view of k-anonymity is clustering with a constraint of the minimum number of objects in every cluster. Most existing approaches to achieving k-anonymity by clustering are for numerical (or ordinal) attributes. In this paper, we stud...

Numerous privacy models based on the k‐anonymity property and extending the k‐anonymity model have been introduced in the last few years in data privacy re‐ search: l‐diversity, p‐sensitive k‐anonymity, (α, k) – anonymity, t‐closeness, etc. While differing in their methods and quality of their results, they all focus first on masking the data, and then protecting the quality of the data as a wh...

In this paper, we first described the concept of k-anonymity and different approaches of its implementation, by formalizing the main theoretical notions. Afterwards, we have analyzed, based on a practical example, how the k-anonymity approach applies to the data-mining process in order to protect the identity and privacy of clients to whom the data refers. We have presented the most important t...

Privacy preservation is an important issue in the release of data for mining purposes. The k-anonymity model has been introduced for protecting individual identification. Recent studies show that a more sophisticated model is necessary to protect the association of individuals to sensitive information. In this paper, we propose an (α, k)-anonymity model to protect both identifications and relat...