Privacy-Preserving Data Publishing

Chen, Bee-Chung; Kifer, Daniel; LeFevre, Kristen; Machanavajjhala, Ashwin

doi:10.1561/1900000008

Cited by 122 publications

(46 citation statements)

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“…12 Therefore, in practice, the implementation of a PPRL protocol has to rely on the enforcement of the corresponding jurisdiction. Composition attacks have been defined by Ganta et al (2008) as attacks, in which an adversary uses independent anonymized releases to breach privacy (for a textbook example with hospital data, see Chen et al 2009). These kind of attacks have received little attention in the PPRL literature, since most protocols minimize the amount of information available for an attacker.…”

Section: Collusionmentioning

confidence: 99%

Privacy‐preserving record linkage

Schnell

2015

Methodological Developments in Data Linkage

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Section: Collusionmentioning

confidence: 99%

Privacy‐preserving record linkage

Schnell

2015

Methodological Developments in Data Linkage

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“…While this may be sufficient for many simple analyses, it may not guarantee that the identity of individuals cannot be revealed from anonymized datasets. Substantial research has been and is currently being performed in the database community on privacy preserving data mining, reflecting the importance of this subject [93][94][95][96][97] (for a comprehensive state-of-the-art summary see the "Privacy-Preserving Data Publishing" survey [98]). Nevertheless, there are still a number of open problems, and many approaches are standing next to each other, lacking user-friendliness, integration, and a consequent systemic approach.…”

Section: Anonymization and Randomizationmentioning

confidence: 99%

Big Data, Privacy, and Trusted Web: What Needs to Be Done

Helbing¹

2015

Thinking Ahead - Essays on Big Data, Digital Revolution, and Participatory Market Society

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“…There are commonly two methods like generalization and suppression mainly used to protect the privacy preservation in data mining. Bee-Chung Chen in [13] demonstrated that releasing a data table by simply removing identifiers (e.g., names and social security numbers) can seriously breach the privacy of individuals whose data are in the table. There are two attacks in k-anonymity.…”

Section: K-anonymitymentioning

confidence: 99%

A Review of Privacy Preservation Technique

Singh¹,

Keer²,

Motwani³

2014

IJCA

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Privacy-preserving is one of the most important challenges in a computer world, because of the huge amount of sensitive information on the internet. The paper contains several privacy preservation techniques for data publishing in the real world. There are several privacy attacks are associate but among of them mainly two attacks are record linkage and attribute linkage. Many scientists have proposed methods to preserve the privacy of data publishing such as K-anonymity, ℓ-diversity, t-closeness. K-anonymity can prevent the record linkage but unable to protect attribute linkage. ℓ-diversity technique overcomes the drawback of k-anonymity technique but it fail to protect from membership discloser attack. Tcloseness technique prevents to attribute discloser attack but it fail in identity disclosure attack. Its computational complexity is large. In this paper we present the novel technique call slicing which to be implemented with various data set through prevent the privacy preservation for data publishing. The goals of this paper is re-analysis a number of privacy preservation of data mining technique clearly and then study the advantages and disadvantages of this technique.

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Privacy-Preserving Data Publishing

Cited by 122 publications

References 0 publications

Privacy‐preserving record linkage

Privacy‐preserving record linkage

Big Data, Privacy, and Trusted Web: What Needs to Be Done

A Review of Privacy Preservation Technique

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