scholarly journals Privacy Preserving Data Mining using Attribute Encryption and Data Perturbation

2013 ◽  
Vol 6 (3) ◽  
pp. 370-378
Author(s):  
Meenakshi Vishnoi ◽  
Seeja K. R
Keyword(s):  
Data Mining ◽  
Personal Information ◽  
Original Data ◽  
Research Area ◽  
Privacy Preserving ◽  
Data Mining Technique ◽  
Privacy Preserving Data Mining ◽  
Mining Technique ◽  
Active Research ◽  
The Individual

Data mining is a very active research area that deals with the extraction of  knowledge from very large databases. Data mining has made knowledge extraction and decision making easy. The extracted knowledge could reveal the personal information , if the data contains various private and sensitive attributes about an individual. This poses a threat to the personal information as there is a possibility of misusing the information behind the scenes without the knowledge of the individual. So, privacy becomes a great concern for the data owners and the organizations  as none of the organizations would like to share their data. To solve this problem Privacy Preserving Data Mining technique have emerged and also solved problems of various domains as it provides the benefit of data mining without compromising the privacy of an individual. This paper proposes a privacy preserving data mining technique the uses randomized perturbation and cryptographic technique. The performance evaluation of the proposed technique shows the same result with the modified data and the original data.

scholarly journals Privacy Preserving In Data Mining with No Data Loss with a Combinational Scheme

2019 ◽  
Vol 8 (4S3) ◽  
pp. 457-459
Keyword(s):  
Data Mining ◽  
Privacy Preserving ◽  
Data Reconstruction ◽  
Sensitive Information ◽  
Data Loss ◽  
Data Mining Technique ◽  
Information Mining ◽  
Mining Technique

Large amounts of data collected by many organizations under-goes data mining for various purposes like analysis and prediction. During data mining tasks, the sensitive information may be losing its privacy. Hence, Privacyprotection or preservation is becomes major issue for the organizations. Publishing data or sharing information for mining with Privacypreservation is possible through Privacypreserve data mining technique (PPDM). Existing techniques are not able to withstand for some attacks and some suffers with data misfortune. In our paper we conventional an effective and combinational approach for security safeguarding in information mining. Our approach with can withstand from different kinds of assaults and limits data misfortune and increases data re-usability with data reconstruction capability

Comprehensive Survey on Privacy Preserving Association Rule Mining: Models, Approaches, Techniques and Algorithms

2014 ◽  
Vol 23 (05) ◽  
pp. 1450004 ◽  
Author(s):  
Ibrahim S. Alwatban ◽  
Ahmed Z. Emam
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Research Area ◽  
Privacy Preserving ◽  
Rule Mining ◽  
Privacy Preserving Data Mining ◽  
Comprehensive Survey ◽  
New Research

In recent years, a new research area known as privacy preserving data mining (PPDM) has emerged and captured the attention of many researchers interested in preventing the privacy violations that may occur during data mining. In this paper, we provide a review of studies on PPDM in the context of association rules (PPARM). This paper systematically defines the scope of this survey and determines the PPARM models. The problems of each model are formally described, and we discuss the relevant approaches, techniques and algorithms that have been proposed in the literature. A profile of each model and the accompanying algorithms are provided with a comparison of the PPARM models.

scholarly journals Random Response Forest for Privacy-Preserving Classification

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Gábor Szűcs
Keyword(s):  
Data Mining ◽  
Random Forest ◽  
Decision Trees ◽  
Original Data ◽  
Privacy Preserving ◽  
Random Response ◽  
Privacy Preserving Data Mining ◽  
Binary Variables ◽  
Binary Decision ◽  
Optimal Coding

The paper deals with classification in privacy-preserving data mining. An algorithm, the Random Response Forest, is introduced constructing many binary decision trees, as an extension of Random Forest for privacy-preserving problems. Random Response Forest uses the Random Response idea among the anonymization methods, which instead of generalization keeps the original data, but mixes them. An anonymity metric is defined for undistinguishability of two mixed sets of data. This metric, the binary anonymity, is investigated and taken into consideration for optimal coding of the binary variables. The accuracy of Random Response Forest is presented at the end of the paper.

Privacy-Preserving Data Mining on the Web

2010 ◽  
pp. 468-481
Author(s):  
Stanley R.M. Oliveira ◽  
Osmar R. Zaïane
Keyword(s):  
Data Mining ◽  
Personal Information ◽  
Privacy Preserving ◽  
Future Research ◽  
Privacy And Security ◽  
Privacy Preserving Data Mining ◽  
The Right To Privacy ◽  
Security Research ◽  
Definition Of ◽  
The Web

Privacy-preserving data mining (PPDM) is one of the newest trends in privacy and security research. It is driven by one of the major policy issues of the information era—the right to privacy. This chapter describes the foundations for further research in PPDM on the Web. In particular, we describe the problems we face in defining what information is private in data mining. We then describe the basis of PPDM including the historical roots, a discussion on how privacy can be violated in data mining, and the definition of privacy preservation in data mining based on users’ personal information and information concerning their collective activities. Subsequently, we introduce a taxonomy of the existing PPDM techniques and a discussion on how these techniques are applicable to Web-based applications. Finally, we suggest some privacy requirements that are related to industrial initiatives and point to some technical challenges as future research trends in PPDM on the Web.

scholarly journals Privacy Preserving Data Mining

2021 ◽  
Author(s):  
Esma Ergüner Özkoç
Keyword(s):  
Data Mining ◽  
Data Privacy ◽  
Personal Data ◽  
Privacy Preserving ◽  
Privacy Preserving Data Mining ◽  
Data Mining Techniques ◽  
Data Mining Algorithms ◽  
Data Output ◽  
The Individual ◽  
Mining Algorithms

Data mining techniques provide benefits in many areas such as medicine, sports, marketing, signal processing as well as data and network security. However, although data mining techniques used in security subjects such as intrusion detection, biometric authentication, fraud and malware classification, “privacy” has become a serious problem, especially in data mining applications that involve the collection and sharing of personal data. For these reasons, the problem of protecting privacy in the context of data mining differs from traditional data privacy protection, as data mining can act as both a friend and foe. Chapter covers the previously developed privacy preserving data mining techniques in two parts: (i) techniques proposed for input data that will be subject to data mining and (ii) techniques suggested for processed data (output of the data mining algorithms). Also presents attacks against the privacy of data mining applications. The chapter conclude with a discussion of next-generation privacy-preserving data mining applications at both the individual and organizational levels.

Privacy-Preserving Data Mining on the Web

2008 ◽  
pp. 50-63 ◽  
Author(s):  
Stanley R.M. Oliveira ◽  
Osmar R. Zaiane
Keyword(s):  
Data Mining ◽  
Personal Information ◽  
Privacy Preserving ◽  
Future Research ◽  
Privacy And Security ◽  
Privacy Preserving Data Mining ◽  
The Right To Privacy ◽  
Security Research ◽  
Definition Of ◽  
The Web

Privacy-preserving data mining (PPDM) is one of the newest trends in privacy and security research. It is driven by one of the major policy issues of the information era—the right to privacy. This chapter describes the foundations for further research in PPDM on the Web. In particular, we describe the problems we face in defining what information is private in data mining. We then describe the basis of PPDM including the historical roots, a discussion on how privacy can be violated in data mining, and the definition of privacy preservation in data mining based on users’ personal information and information concerning their collective activities. Subsequently, we introduce a taxonomy of the existing PPDM techniques and a discussion on how these techniques are applicable to Web-based applications. Finally, we suggest some privacy requirements that are related to industrial initiatives and point to some technical challenges as future research trends in PPDM on the Web.

scholarly journals A review on Privacy Preservation and Collaborative Data Mining

2015 ◽  
Vol 14 (12) ◽  
pp. 6368-6372
Author(s):  
Amit Kumar
Keyword(s):  
Data Mining ◽  
Data Transmission ◽  
Privacy Preservation ◽  
Current Data ◽  
Sensitive Information ◽  
Data Mining Technique ◽  
Privacy Preserving Data Mining ◽  
Mining Technique ◽  
Cloud Network ◽  
Collaborative Data Mining

Privacy preservation is major issue in current data transmission over internet and cloud network. For the integrity and security of data various methods are used such as cryptography, data transformation, Steganography, watermarking and many more method. In consequence of all these method some data mining technique is used. The data mining technique provide Varity of algorithm for privacy preservation. The collaborative data mining technique used different agent method for the integrity of security of data during transmission. Issues about privacy-preserving data mining have emerged globally, but still the main problem is that non- sensitive information or unclassified data, one is able to infer sensitive information that is not supposed to be disclosed. Data collection is a necessary step in data mining process. Due to privacy reasons, collecting data from different parries becomes difficult. In this paper presents the review of privacy persevering technique used data mining.

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