Attacks on Anonymization-Based Privacy-Preserving: A Survey for Data Mining and Data Publishing

Hussien, Abou-el-ela Abdou; Hamza, Nermin; Hefny, Hesham A.

doi:10.4236/jis.2013.42012

Cited by 31 publications

(11 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Second Suppression involves not releasing a value at all [13]. It is clear that such methods reduce the risk of identification with the use of public records, while reducing the accuracy of applications on the transformed data.…”

Section: K-anonymity Generalization and Suppressionmentioning

confidence: 99%

Utility-Based Anonymization Using Generalization Boundaries to Protect Sensitive Attributes

Hussien¹,

Darwish²,

Hefny³

2015

JIS

Self Cite

View full text Add to dashboard Cite

Privacy preserving data mining (PPDM) has become more and more important because it allows sharing of privacy sensitive data for analytical purposes. A big number of privacy techniques were developed most of which used the k-anonymity property which have many shortcomings, so other privacy techniques were introduced (l-diversity, p-sensitive k-anonymity, (α, k)-anonymity, tcloseness, etc.). While they are different in their methods and quality of their results, they all focus first on masking the data, and then protecting the quality of the data. This paper is concerned with providing an enhanced privacy technique that combines some anonymity techniques to maintain both privacy and data utility by considering the sensitivity values of attributes in queries using sensitivity weights which determine taking in account utility-based anonymization and then only queries having sensitive attributes whose values exceed threshold are to be changed using generalization boundaries. The threshold value is calculated depending on the different weights assigned to individual attributes which take into account the utility of each attribute and those particular attributes whose total weights exceed the threshold values is changed using generalization boundaries and the other queries can be directly published. Experiment results using UT dallas anonymization toolbox on real data set adult database from the UC machine learning repository show that although the proposed technique preserves privacy, it also can maintain the utility of the publishing data.

show abstract

Section: K-anonymity Generalization and Suppressionmentioning

confidence: 99%

Utility-Based Anonymization Using Generalization Boundaries to Protect Sensitive Attributes

Hussien¹,

Darwish²,

Hefny³

2015

JIS

Self Cite

View full text Add to dashboard Cite

show abstract

“…Privacy preserving data mining is going to be achieved in different ways specifically by using randomization methods, cryptography algorithms and anonymization methods. A modern survey on are being used on various methods using privacy preserving data mining are found [1] in which reviews major PPDM techniques based on merits and demerits on recent trends in PPDM. The current scenario privacy preserving data mining [2] propose some future research directions for research people.…”

Section: Privacy Preserving Data Miningmentioning

confidence: 99%

Survey on Privacy Preserving Data Mining Techniques using Recent Algorithms

Rajesh¹,

Sujatha²,

Selvakumar³

2016

IJCA

View full text Add to dashboard Cite

The privacy preserving data mining is playing crucial role act as rising technology to perform various data mining operations on private data and to pass on data in a secured way to protect sensitive data. Many types of technique such as randomization, secured sum algorithms and k-anonymity have been suggested in order to execute privacy preserving data mining. In this survey paper, on current researches made on privacy preserving data mining technique with fuzzy logic, neural network learning, secured sum and various encryption algorithm is presented. This will enable to grasp the various challenges faced in privacy preserving data mining and also help us to find best suitable technique for various data environment.

show abstract

“…In [1] various techniques of PPDM is analyzed as shown in Table 1. In [2], data Perturbation for PPDM is analyzed and in [3] various attacks in PPDM is discussed. In [4,5], fuzzy based PPDM is proposed and in [6,7], PPDM is proposed.…”

Section: Introductionmentioning

confidence: 99%

Privacy Preserving Data Mining With Multi Keyword Ranked Search for Medical Data

Subhashree*,

Gunasekaran

2020

IJITEE

View full text Add to dashboard Cite

Privacy Preserving Data Mining (PPDM) maintains the privacy of data stored in cloud. This work aims to protect outsourced data in cloud, and also permit multi keyword search over the encrypted data in a secure way by NLP process without downloading and decrypting all files. Different methods for privacy preservation were analyzed and randomization for multilevel trust is proposed along with an efficient method for keyword search in cloud.

show abstract

Attacks on Anonymization-Based Privacy-Preserving: A Survey for Data Mining and Data Publishing

Cited by 31 publications

References 30 publications

Utility-Based Anonymization Using Generalization Boundaries to Protect Sensitive Attributes

Utility-Based Anonymization Using Generalization Boundaries to Protect Sensitive Attributes

Survey on Privacy Preserving Data Mining Techniques using Recent Algorithms

Privacy Preserving Data Mining With Multi Keyword Ranked Search for Medical Data

Contact Info

Product

Resources

About