scholarly journals Efficient Anonymization Algorithm for Multiple Sensitive Attributes

2019 ◽  
Vol 9 (1) ◽  
pp. 4961-4963
Keyword(s):  
Data Mining ◽  
Performance Evaluation ◽  
Information Loss ◽  
The Internet ◽  
Medical Applications ◽  
Huge Number ◽  
Sensitive Data ◽  
Privacy Preserving Data Mining ◽  
Privacy Model ◽  
Mining Methods

The data of medical applications over the internet contains sensitive data. There exist several methods that provide privacy for these data. Most of the privacy-preserving data mining methods make the assumption of the separation of quasi-identifiers (QID) from multiple sensitive attributes. But in reality, the attributes in a dataset possess both the features of QIDs and sensitive data. In this paper privacy model namely (vi…vj)-diversity is proposed. The proposed anonymization algorithm works for databases containing numerous sensitive QIDs. The real dataset is used for performance evaluation. Our system reduced the information loss for even huge number of attributes and the values of sensitive QID’s are protected.

Privacy-Preserving Data Mining and the Need for Confluence of Research and Practice

2008 ◽  
pp. 2402-2420
Author(s):  
Lixin Fu ◽  
Hamid Nemati ◽  
Fereidoon Sadri
Keyword(s):  
Data Mining ◽  
Research Agenda ◽  
Information Technologies ◽  
Privacy Preserving ◽  
Theory And Practice ◽  
Clear Understanding ◽  
Sensitive Data ◽  
Privacy Preserving Data Mining ◽  
Research And Practice ◽  
Rapid Pace

Privacy-preserving data mining (PPDM) refers to data mining techniques developed to protect sensitive data while allowing useful information to be discovered from the data. In this article, we review PPDM and present a broad survey of related issues, techniques, measures, applications, and regulation guidelines. We observe that the rapid pace of change in information technologies available to sustain PPDM has created a gap between theory and practice. We posit that without a clear understanding of the practice, this gap will be widening which, ultimately, will be detrimental to the field. We conclude by proposing a comprehensive research agenda intended to bridge the gap relevant to practice and as a reference basis for the future related legislation activities.

Semantically Secure Classifiers for Privacy Preserving Data Mining

2021 ◽  
pp. 1066-1095
Author(s):  
Sumana M. ◽  
Hareesha K. S. ◽  
Sampath Kumar
Keyword(s):  
Data Mining ◽  
Model Building ◽  
Computation Time ◽  
Privacy Preserving ◽  
Sensitive Data ◽  
Privacy Preserving Data Mining ◽  
Intermediate Data ◽  
Secure Protocols ◽  
Mean And Variance ◽  
Probabilistic Property

Essential predictions are to be made by the parties distributed at multiple locations. However, in the process of building a model, perceptive data is not to be revealed. Maintaining the privacy of such data is a foremost concern. Earlier approaches developed for classification and prediction are proven not to be secure enough and the performance is affected. This chapter focuses on the secure construction of commonly used classifiers. The computations performed during model building are proved to be semantically secure. The homomorphism and probabilistic property of Paillier is used to perform secure product, mean, and variance calculations. The secure computations are performed without any intermediate data or the sensitive data at multiple sites being revealed. It is observed that the accuracy of the classifiers modeled is almost equivalent to the non-privacy preserving classifiers. Secure protocols require reduced computation time and communication cost. It is also proved that proposed privacy preserving classifiers perform significantly better than the base classifiers.

scholarly journals Website evaluation using opinion mining

2018 ◽  
Vol 7 (2.7) ◽  
pp. 51
Author(s):  
T V.R. Sai ◽  
SK Haaris ◽  
S Sridevi
Keyword(s):  
Data Mining ◽  
Opinion Mining ◽  
User Feedback ◽  
The Internet ◽  
Website Evaluation ◽  
The World ◽  
Frame Work ◽  
Mining Methods ◽  
Feedback Data

In this project we used opinion mining methods to evaluate various websites present on the internet. We also analyzed the approaches, tools, and dataset used by Scholars with their accuracy and we used this technology for evaluation of a website. Opinion mining is used in various scenarios around the world. But it is hardly used in websites evaluation which we are implementing with this project, as now a day’s, websites we regularly use are spamming with advertisements and unusable content. This paper proposed a frame work of evaluating a website using the user feedback on the website collected on our website. That collected feedback data is processed using a data mining software that is rapid miner. 

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