Spatial K-anonymity (SKA) exploits the concept of K-anonymity in order to protect the identity of users from location-based attacks. The main idea of SKA is to replace the exact location of a user U with an anonymizing spatial region (ASR) that contains at least K-1 other users, so that an attacker can pinpoint U with probability at most 1/K. Simply generating an ASR that includes K users does ...

The identity of patients must be protected when patient data are shared. The two most commonly used models to protect identity of patients are L-diversity and K-anonymity. However, existing work mainly considers data sets with a single sensitive attribute, while patient data often contain multiple sensitive attributes (e.g., diagnosis and treatment). This article shows that although the K-anony...

In this paper we describe KANIS, a distributed system designed to preserve the privacy of multidimensional, hierarchical data that are dispersed over a network. While allowing for efficient storing, indexing and querying of the data, our system employs an adaptive scheme that automatically adjusts the level of indexing according to the privacy constrains: Efficient roll-up and drill-down operat...

Publishing data for analysis from a microdata table containing sensitive attributes, while maintaining individual privacy, is a problem of increasing significance today. The k-anonymity model was proposed for privacy preserving data publication. While focusing on identity disclosure, k-anonymity model fails to protect attribute disclosure to some extent. Many efforts are made to enhance the kan...

How to protect individual privacy in public data is always a concern. For social networks, the challenge is that, the structure of the social network graph can be utilized to infer the private and sensitive information of users. The existing anonymity schemes mostly focus on the anonymity of vertex identities, such that a malicious attacker cannot associate an user with a specific vertex. In re...

Before statistical data, such as microdata, can be released to the public, it needs to be anonymised. Anonymisation protects the privacy of the individuals whose data is released. However, as microdata is anonymised, its level of privacy increases, while its level of information utility decreases. K-anonymity is often used to address the conflict between privacy and information utility in micro...

The Access control mechanism avoids the unauthorized access of sensitive information. It protects the user information from the unauthorized access. The privacy protection mechanism is a much important concern in the case of sharing the sensitive information. The privacy protection mechanism provides better privacy for the sensitive information which is to be shared. The generally used privacy ...

Protecting privacy in microdata publishing is K-Anonymity, Here recoding “models” have been considered for achieving k anonymity[1,2]. We proposes a new multidimensional model, which gives high flexibility. Often this flexibility leads to higher-quality anonymizations, as measured both by general-purpose metrics and more specific notions of query answerability. Like previous multidimensional mo...

Data privacy preservation has drawn considerable interests in data mining research recently. The k-anonymity model is a simple and practical approach for data privacy preservation. This paper proposes a novel clustering method for conducting the k-anonymity model effectively. In the proposed clustering method, feature weights are automatically adjusted so that the information distortion can be ...

Source locations of events are sensitive contextual information that needs to be protected in sensor networks. Previous work focuses on either an active local attacker that traces back to a real source in a hop-by-hop fashion, or a passive global attacker that eavesdrops/analyzes all network traffic to discover real sources. An active global attack model, which is more realistic and powerful th...