Fuzzy Microaggregation for Microdata Protection

Domingo‐Ferrer, Josep; Torra, Vicenç

doi:10.20965/jaciii.2003.p0153

“…Steps similar to the HM method are then applied to generate clusters based on this ordering. Domingo-Ferrer et al [7] proposed a microaggregation method based on fuzzy c-means algorithm (FCM) [1]. This method repeatedly runs FCM to adjust the two parameters of FCM (one is the number of clusters c and another is the exponent for the partition matrix m) until each cluster contains at least k records.…”

Section: Previous Microaggregation Methodsmentioning

confidence: 99%

“…Various methods for Microaggregation has been proposed in the literature for protecting microdata [3,4,7,8,11,12,20,22]. The basic idea of microaggregation is to partition a dataset into mutually exclusive groups of at least k records prior to publication, and then publish the centroid over each group instead of individual records.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Domingo-Ferrer and Mateo-Sanz [3] proposed univariate and multivariate k-Ward algorithms that extend the agglomerative hierarchical clustering method of Ward et al [23]. Domingo-Ferrer and Torra [6,7] proposed a microaggregation method based on the fuzzy c-means algorithm [1], and Laszlo and Mukherjee [13] extended the standard minimum spanning tree partitioning algorithm for microaggregation [26]. All of these microaggregation methods build all clusters gradually but simultaneously.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Novel Iterative Min-Max Clustering to Minimize Information Loss in Statistical Disclosure Control

Mahmood

¹

,

Kabir

²

,

Mustafa

³

2015

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

1

0

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Abstract. 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 linked to specific individuals. Microaggregation works by partitioning the microdata into groups of at least k records and then replacing the records in each group with the centroid of the group. An optimal microaggregation method must minimize the information loss resulting from this replacement process. The challenge is how to minimize the information loss during the microaggregation process. This paper presents a new microaggregation technique for Statistical Disclosure Control (SDC). It consists of two stages. In the first stage, the algorithm sorts all the records in the data set in a particular way to ensure that during microaggregation very dissimilar observations are never entered into the same cluster. In the second stage an optimal microaggregation method is used to create k-anonymous clusters while minimizing the information loss. It works by taking the sorted data and simultaneously creating two distant clusters using the two extreme sorted values as seeds for the clusters. The performance of the proposed technique is compared against the most recent microaggregation methods. Experimental results using benchmark datasets show that the proposed algorithm has the lowest information loss compared with a basket of techniques in the literature.

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“…Various methods for Microaggregation has been proposed in the literature for protecting microdata [3,4,7,8,11,12,20,23]. The basic idea of microaggregation is to partition a dataset into mutually exclusive groups of at least k records prior to publication, and then publish the centroid over each group instead of individual records.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Domingo-Ferrer and Mateo-Sanz [3] proposed univariate and multivariate k-Ward algorithms that extend the agglomerative hierarchical clustering method of Ward et al [24]. Domingo-Ferrer and Torra [6,7] proposed a microaggregation method based on the fuzzy c-means algorithm [1], and Laszlo and Mukherjee [13] extended the standard minimum spanning tree partitioning algorithm for microaggregation [27]. All of these microaggregation methods build all clusters gradually but simultaneously.…”

Section: Introductionmentioning

confidence: 99%

New Multi-dimensional Sorting Based K-Anonymity Microaggregation for Statistical Disclosure Control

Mahmood

¹

,

Kabir

²

,

Mustafa

³

2013

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Abstract. 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 linked to specific individuals. Microaggregation works by partitioning the microdata into groups of at least k records and then replacing the records in each group with the centroid of the group. An optimal microaggregation method must minimize the information loss resulting from this replacement process. The challenge is how to minimize the information loss during the microaggregation process. This paper presents a new microaggregation technique for Statistical Disclosure Control (SDC). It consists of two stages. In the first stage, the algorithm sorts all the records in the data set in a particular way to ensure that during microaggregation very dissimilar observations are never entered into the same cluster. In the second stage an optimal microaggregation method is used to create k-anonymous clusters while minimizing the information loss. It works by taking the sorted data and simultaneously creating two distant clusters using the two extreme sorted values as seeds for the clusters. The performance of the proposed technique is compared against the most recent microaggregation methods. Experimental results using benchmark datasets show that the proposed algorithm has the lowest information loss compared with a basket of techniques in the literature.

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A survey on statistical disclosure control and micro‐aggregation techniques for secure statistical databases

Fayyoumi¹,

Oommen

²

2010

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This paper surveys the fields of Statistical Disclosure Control (SDC) and Micro‐Aggregation Techniques (MATs), which are both areas fundamental to the science of secure Statistical DataBases (SDBs). The paper is written from the perspective of a computer scientist with the hope that it will prove to be a source of reference material useful to researchers and practitioners in the field. The paper first introduces the concept of SDC and describes the domain of its applications and the various data types that are currently used in SDBs. It then proceeds to focus on the family of micro‐data types in SDBs. At this juncture, we introduce the importance of the relevant measures, namely the metrics termed as the Information Loss (IL) and the Disclosure Risk (DR), after which we survey the various methods of resolving the conflicting goals that these metrics represent. Thereafter, the paper summarizes the perturbative and non‐perturbative SDC methods for micro‐data protection, and it focuses on the families of MATs by formally stating the Micro‐Aggregation Problem and surveying it in a comprehensive manner. Apart from the paper including a historical view of the field of MATs, it describes a broad selection of work that has been reported more recently. Indeed, we believe that this paper represents a complete overview of the state‐of‐the‐art techniques. Copyright © 2010 John Wiley & Sons, Ltd.

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Fuzzy Microaggregation for Microdata Protection

Cited by 18 publications

References 12 publications

Novel Iterative Min-Max Clustering to Minimize Information Loss in Statistical Disclosure Control

Novel Iterative Min-Max Clustering to Minimize Information Loss in Statistical Disclosure Control

New Multi-dimensional Sorting Based K-Anonymity Microaggregation for Statistical Disclosure Control

A survey on statistical disclosure control and micro‐aggregation techniques for secure statistical databases

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