3,407 research outputs found
Generalization-Based k-Anonymization
Microaggregation is an anonymization technique consisting on partitioning the data into clusters no smaller than k elements and then replacing the whole cluster by its prototypical representant. Most of microaggregation techniques work on numerical attributes. However, many data sets are described by heterogeneous types of data, i.e., nu- merical and categorical attributes. In this paper we propose a new mi- croaggregation method for achieving a compliant k-anonymous masked file for categorical microdata based on generalization. The goal is to build a generalized description satisfied by at least k domain objects and to replace these domain objects by the description. The way to construct that generalization is similar that the one used in growing decision trees. Records that cannot be generalized satisfactorily are discarded, therefore some information is lost. In the experiments we performed we prove that the new approach gives good results. © Springer International Publishing Switzerland 2015.This research is partially funded by the Spanish MICINN projects COGNITIO (TIN-2012-38450-C03-03), EdeTRI (TIN2012-39348-C02-01) and COPRIVACY (TIN2011-27076-C03-03), the grant 2009-SGR-1434 from the Generalitat de Catalunya, and the European Project DwB (Grant Agreement Number 262608)Peer reviewe
Publishing Microdata with a Robust Privacy Guarantee
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 value therein. This paper introduces beta-likeness, an appropriately
robust privacy model for microdata anonymization, along with two anonymization
schemes designed therefor, the one based on generalization, and the other based
on perturbation. Our model postulates that an adversary's confidence on the
likelihood of a certain sensitive-attribute (SA) value should not increase, in
relative difference terms, by more than a predefined threshold. Our techniques
aim to satisfy a given beta threshold with little information loss. We
experimentally demonstrate that (i) our model provides an effective privacy
guarantee in a way that predecessor models cannot, (ii) our generalization
scheme is more effective and efficient in its task than methods adapting
algorithms for the k-anonymity model, and (iii) our perturbation method
outperforms a baseline approach. Moreover, we discuss in detail the resistance
of our model and methods to attacks proposed in previous research.Comment: VLDB201
Towards trajectory anonymization: a generalization-based approach
Trajectory datasets are becoming popular due to the massive usage of GPS and locationbased services. In this paper, we address privacy issues regarding the identification of individuals in static trajectory datasets. We first adopt the notion of k-anonymity to trajectories and propose a novel generalization-based approach for anonymization of trajectories. We further show that releasing
anonymized trajectories may still have some privacy leaks. Therefore we propose a randomization based reconstruction algorithm for releasing anonymized trajectory data and also present how the underlying techniques can be adapted to other anonymity standards. The experimental results on real and synthetic trajectory datasets show the effectiveness of the proposed techniques
Hybrid Anonymization Technique For Improving The Privacy In Network Data
There has been a considerable research over the last decades on methods for limiting disclosure in data publishing, especially for the last twenty years in the computer science field. Researchers have studied the problems of publishing microdata or network data without revealing any sensitive information that may have cause the paradigm preservation of information privacy. There are organizations that would like to publish their data for research, advertisement or prediction purposes. Nevertheless, they had the problems in information loss and lack of privacy. Hence, there are a few techniques and research that have been in highlights like the K-anonymity, l-diversity, generalization, clustering and randomization techniques, but most of these techniques is not comprehensive and the chances to lose the information is still high and may cause privacy leakage on the original data. The contribution of this research is the hybrid technique in anonymization process that will improve the protection and the privacy of data. With this better and comprehensive solution, it will decrease the loss of information. There are four major phases in this methodology as research guidance. The first phase is an overview of the entire research process and the second phase is the description of the anonymization process and techniques. It will be followed by the third phase of describing the design and module of the system, and the fourth phase is the researcher highlights on the comparison methods that are designed in this study. The researcher stated that there are two main contributions in this research. The first contribution is to introduce a new technique to anonymize the network data using the hybrid technique; and for the second contribution, the researcher creates a profile of a hybrid anonymization technique based on K-anonymity, l-diversity, generalization, clustering and randomization techniques. It is quite difficult to identify the best technique of anonymization process. Due to this, the researcher provides the details of analyzing, summarizing and profiling of the anonymization techniques. The researcher realizes that there are a few opportunities to advance this research within this domain in the near future, such as implementing a real-time based in anonymization process. Unfortunately, this type of processing needs to be revamped from the architectural design until the data processing part; and it is more thought-provoking if it were implemented in a real-time based or in the batch processing process, if the variable of the optimization is to be used in the anonymization process. Apart from that, the profiling of the anonymization processing techniques will also help the researcher to propose a generalization technique that might be implemented to anonymize data either using the micro or the network data
Ontology-Based Quality Evaluation of Value Generalization Hierarchies for Data Anonymization
In privacy-preserving data publishing, approaches using Value Generalization
Hierarchies (VGHs) form an important class of anonymization algorithms. VGHs
play a key role in the utility of published datasets as they dictate how the
anonymization of the data occurs. For categorical attributes, it is imperative
to preserve the semantics of the original data in order to achieve a higher
utility. Despite this, semantics have not being formally considered in the
specification of VGHs. Moreover, there are no methods that allow the users to
assess the quality of their VGH. In this paper, we propose a measurement
scheme, based on ontologies, to quantitatively evaluate the quality of VGHs, in
terms of semantic consistency and taxonomic organization, with the aim of
producing higher-quality anonymizations. We demonstrate, through a case study,
how our evaluation scheme can be used to compare the quality of multiple VGHs
and can help to identify faulty VGHs.Comment: 18 pages, 7 figures, presented in the Privacy in Statistical
Databases Conference 2014 (Ibiza, Spain
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