Privacy Issues for K-anonymity Model
نویسندگان
چکیده
K-anonymity is the approach used for preventing identity disclosure. Identity disclosure means an individual is linked to a particular record in the published data and individual’s sensitive data is accessed .Some important information such as Name, Income details , Medical Status and Property details are considered as a sensitive data( or Attribute) because these data have to be kept secure from unauthorized access. Generally these details are stored in private tables of any organization or committees. Some released attributes called as quasi identifiers (Zip code, Sex, marital status, Age, Date of Birth, Bank details) when linked with private table cause the Identity disclosure. In this paper we will discuss some privacy issues for k-anonymity model and check its integrity while using some approaches. KeywordsK-anonymity model ,Attacks ,l-diversity,tcloseness, Sensitive tuples.
منابع مشابه
Improved Univariate Microaggregation for Integer Values
Privacy issues during data publishing is an increasing concern of involved entities. The problem is addressed in the field of statistical disclosure control with the aim of producing protected datasets that are also useful for interested end users such as government agencies and research communities. The problem of producing useful protected datasets is addressed in multiple computational priva...
متن کاملEnhanced P-Sensitive K-Anonymity Models for Privacy Preserving Data Publishing
Publishing data for analysis from a micro data 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 k-...
متن کاملResearch on Privacy Preserving on K-anonymity
The disclosure of sensitive information has become prominent nowadays; privacy preservation has become a research hotspot in the field of data security. Among all the algorithms of privacy preservation in data mining, K-anonymity is a kind of common and valid algorithm in privacy preservation, which can effectively prevent the loss of sensitive information under linking attacks, and it is widel...
متن کاملGenerating Microdata with P -Sensitive K -Anonymity Property
Existing privacy regulations together with large amounts of available data have created a huge interest in data privacy research. A main research direction is built around the k-anonymity property. Several shortcomings of the k-anonymity model have been fixed by new privacy models such as p-sensitive k-anonymity, l-diversity, (α, k)-anonymity, and t-closeness. In this paper we introduce the Enh...
متن کاملAn Anonymity Model Achievable Via Microaggregation
k-Anonymity is a privacy model requiring that all combinations of key attributes in a database be repeated at least for k records. It has been shown that k-anonymity alone does not always ensure privacy. A number of sophistications of k-anonymity have been proposed, like p-sensitive k-anonymity, l-diversity and t-closeness. We identify some shortcomings of those models and propose a new model c...
متن کامل