Majority of the search algorithms in microdata anonymization restrict themselves to a single privacy property and a single criteria to optimize. The solutions obtained are therefore of limited application since adherence to multiple privacy models is required to impede different forms of privacy attacks. Towards this end, we propose the concept of a property based generalization (PBG) to captur...

BACKGROUND Anonymisation of spatially referenced data has received increasing attention in recent years. Whereas the research focus has been on the anonymisation of point locations, the disclosure risk arising from the publishing of inter-point distances and corresponding anonymisation methods have not been studied systematically. METHODS We propose a new anonymisation method for the release ...

Many statistical agencies disseminate samples of census microdata, i.e., data on individual records, to the public. Before releasing the microdata, agencies typically alter identifying or sensitive values to protect data subjects’ confidentiality, for example by coarsening, perturbing, or swapping data. These standard disclosure limitation techniques distort relationships and distributional fea...

This study analyzes business performance through efficiency score estimation in two sustainable tourism models: cultural and rural tourism. The uses microeconomic data (microdata) of Spanish firms with different characteristics terms size, region, location financial variables. Using multistage modeling (Data envelopment analysis, DEA, non-parametric frontier tests), the main results show that a...

Often microdata sets contain attributes which are neither numerical nor ordinal, but take nominal values from a taxonomy, ontology or classification (e.g. diagnosis in a medical data set about patients, economic activity in an economic data set, etc.). Such data sets must be anonymized if transferred outside the data collector’s premises (e.g. hospital or national statistical office), say, for ...

Today, the publication of microdata poses a privacy threat. Vast research has striven to define the privacy condition that microdata should satisfy before it is released, and devise algorithms to anonymize the data so as to achieve this condition. Yet, no method proposed to date explicitly bounds the percentage of information an adversary gains after seeing the published data for each sensitive...

A number of organizations publish microdata for purposes such as public health and demographic research. Although attributes of microdata that clearly identify individuals, such as name and medical care card number, are generally removed, these databases can sometimes be joined with other public databases on attributes such as Zip code, Gender and Age to reidentify individuals who were supposed...

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 recent years, there has been an alarming increase of online identity theft and attacks using personally identifiable information. The goal of privacy preservation is to de-associate individuals from sensitive or microdata information. Microaggregation techniques seeks to protect microdata in such a way that can be published and mined without providing any private information that can be link...

This article presents the anatomy technique for anonymized publication of sensitive data. Anatomy releases all the quasi-identifier and sensitive values directly in two separate tables. Combined with a grouping mechanism, this approach effectively protects privacy, and captures a large amount of correlation in the microdata. We propose an efficient algorithm for computing anatomized tables that...