In this paper we consider the problem of anonymizing datasets in which each individual is associated with a set of items that constitute private information about the individual. Illustrative datasets include market-basket datasets and search engine query logs. We formalize the notion of k-anonymity for set-valued data as a variant of the k-anonymity model for traditional relational datasets. W...

Objective: The authors developed and empirically evaluated a new globally optimal de-identification algorithm that satisfies the k-anonymity criterion and that is suitable for health datasets. Design: Authors compared OLA (Optimal Lattice Anonymization) empirically to three existing k-anonymity algorithms, Datafly, Samarati, and Incognito, on six public, hospital, and registry datasets for diff...

Identity disclosure is one of the most serious privacy concerns in today’s information age. A wellknow method for protecting identity disclosure is k-anonymity. A dataset provides k-anonymity protection if the information for each individual in the dataset cannot be distinguished from at least k – 1 individuals whose information also appears in the dataset. There is a flaw in kanonymity that wo...

Nowadays, information sharing as an indispensable part appears in our vision, bringing about a mass of discussions about methods and techniques of privacy preserving data publishing which are regarded as strong guarantee to avoid information disclosure and protect individuals’ privacy. Recent work focuses on proposing different anonymity algorithms for varying data publishing scenarios to satis...

k-anonymity is the method used for masking sensitive data which successfully solves the problem of re-linking of data with an externa l source and makes it difficul t to l'e-iden tify the individual. T hus kanonymity works on a set of quasi-identifiers (public sensitive at t ributes), whose possible availability and linking is anticipated from external dataset , and demands that the released da...

As more and more person-specific data like health information becomes available, increasing attention is being paid to confidentiality and privacy protection. One proposed measure of confidentiality is k-anonymity, where a dataset is k-anonymous if each record is identical to at least (k-1) others in the dataset. All known algorithms for k-anonymization or even checking the degree of k-anonymit...

The concept of k-anonymity protection model has been proposed as an effective way to protect the identities of subjects in a disclosed database. However, from a k-anonymous dataset it may be possible to directly infer private data. This direct disclosure is called attribute linkage. k-anonymity also suffer to another form of attack based on data mining results. In fact, data mining models and p...

This paper treats the privacy-preserving publication of social graphs in the presence of active adversaries, that is, adversaries with the ability to introduce sybil nodes in the graph prior to publication and leverage them to create unique fingerprints for a set of victim nodes and re-identify them after publication. Stemming from the notion of (k, l)-anonymity, we introduce (k, l)-anonymous t...