The Study of Privacy Preserving Data Mining Technology for Information Security

2014 ◽  
Vol 556-562 ◽  
pp. 3532-3535
Author(s):  
Heng Li ◽  
Xue Fang Wu
Keyword(s):  
Data Mining ◽  
Data Privacy ◽  
Privacy Preservation ◽  
Rapid Development ◽  
Privacy Preserving ◽  
Future Research ◽  
Privacy Preserving Data Mining ◽  
Mining Technology ◽  
Network Database ◽  
Use Of Data

With the rapid development of computer technology and the popularity of the network, database scale, scope and depth of the constantly expanding, which has accumulated vast amounts of different forms of stored data. The use of data mining technology can access valuable information from a lot of data. Privacy preserving has been one of the greater concerns in data mining. Privacy preserving data mining has a rapid development in a short year. But it still faces many challenges in the future. A number of methods and techniques have been developed for privacy preserving data mining. This paper analyzed the representative techniques for privacy preservation. Finally the present problems and directions for future research are discussed.

Study on distributed privacy preserving data mining

2014 ◽  
Vol 11 (2) ◽  
pp. 163-170
Author(s):  
Binli Wang ◽  
Yanguang Shen
Keyword(s):  
Data Mining ◽  
Data Privacy ◽  
Rapid Development ◽  
Privacy Preserving ◽  
Future Research ◽  
Distributed Data ◽  
Distributed Environment ◽  
Privacy Preserving Data Mining ◽  
Advantages And Disadvantages ◽  
Future Research Directions

Recently, with the rapid development of network, communications and computer technology, privacy preserving data mining (PPDM) has become an increasingly important research in the field of data mining. In distributed environment, how to protect data privacy while doing data mining jobs from a large number of distributed data is more far-researching. This paper describes current research of PPDM at home and abroad. Then it puts emphasis on classifying the typical uses and algorithms of PPDM in distributed environment, and summarizing their advantages and disadvantages. Furthermore, it points out the future research directions in the field.

Privacy Preserving Data Mining, Concepts, Techniques, and Evaluation Methodologies

2008 ◽  
pp. 2379-2401 ◽  
Author(s):  
Igor Nai Fovino
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Privacy Preserving ◽  
Research Field ◽  
Sensitive Information ◽  
Discovery Process ◽  
Privacy Preserving Data Mining ◽  
Mining Technology ◽  
Evaluation Methodologies ◽  
New Research

Intense work in the area of data mining technology and in its applications to several domains has resulted in the development of a large variety of techniques and tools able to automatically and intelligently transform large amounts of data in knowledge relevant to users. However, as with other kinds of useful technologies, the knowledge discovery process can be misused. It can be used, for example, by malicious subjects in order to reconstruct sensitive information for which they do not have an explicit access authorization. This type of “attack” cannot easily be detected, because, usually, the data used to guess the protected information, is freely accessible. For this reason, many research efforts have been recently devoted to addressing the problem of privacy preserving in data mining. The mission of this chapter is therefore to introduce the reader in this new research field and to provide the proper instruments (in term of concepts, techniques and example) in order to allow a critical comprehension of the advantages, the limitations and the open issues of the Privacy Preserving Data Mining Techniques.

Privacy-Preserving Data Mining

2008 ◽  
pp. 693-704
Author(s):  
Bhavani Thuraisingham
Keyword(s):  
Data Mining ◽  
Privacy Preserving ◽  
Future Research ◽  
Inference Problem ◽  
Privacy Preserving Data Mining ◽  
Privacy Concerns ◽  
Security Applications ◽  
Constraint Processing ◽  
Privacy Constraints ◽  
Privacy Problem

This article first describes the privacy concerns that arise due to data mining, especially for national security applications. Then we discuss privacy-preserving data mining. In particular, we view the privacy problem as a form of inference problem and introduce the notion of privacy constraints. We also describe an approach for privacy constraint processing and discuss its relationship to privacy-preserving data mining. Then we give an overview of the developments on privacy-preserving data mining that attempt to maintain privacy and at the same time extract useful information from data mining. Finally, some directions for future research on privacy as related to data mining are given.

Privacy Preserving Data Mining, Concepts, Techniques, and Evaluation Methodologies

2008 ◽  
pp. 277-301
Author(s):  
Igor Nai Fovino
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Privacy Preserving ◽  
Research Field ◽  
Sensitive Information ◽  
Discovery Process ◽  
Privacy Preserving Data Mining ◽  
Mining Technology ◽  
Evaluation Methodologies ◽  
New Research

Intense work in the area of data mining technology and in its applications to several domains has resulted in the development of a large variety of techniques and tools able to automatically and intelligently transform large amounts of data in knowledge relevant to users. However, as with other kinds of useful technologies, the knowledge discovery process can be misused. It can be used, for example, by malicious subjects in order to reconstruct sensitive information for which they do not have an explicit access authorization. This type of “attack” cannot easily be detected, because, usually, the data used to guess the protected information, is freely accessible. For this reason, many research efforts have been recently devoted to addressing the problem of privacy preserving in data mining. The mission of this chapter is therefore to introduce the reader in this new research field and to provide the proper instruments (in term of concepts, techniques and example) in order to allow a critical comprehension of the advantages, the limitations and the open issues of the Privacy Preserving Data Mining Techniques.

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.

Privacy-Preserving Data Mining

2008 ◽  
pp. 627-638 ◽  
Author(s):  
Bhavani Thuraisingham
Keyword(s):  
Data Mining ◽  
Privacy Preserving ◽  
Future Research ◽  
Inference Problem ◽  
Privacy Preserving Data Mining ◽  
Privacy Concerns ◽  
Security Applications ◽  
Constraint Processing ◽  
Privacy Constraints ◽  
Privacy Problem

This article first describes the privacy concerns that arise due to data mining, especially for national security applications. Then we discuss privacy-preserving data mining. In particular, we view the privacy problem as a form of inference problem and introduce the notion of privacy constraints. We also describe an approach for privacy constraint processing and discuss its relationship to privacy-preserving data mining. Then we give an overview of the developments on privacy-preserving data mining that attempt to maintain privacy and at the same time extract useful information from data mining. Finally, some directions for future research on privacy as related to data mining are given.

Secure Computation for Privacy Preserving Data Mining

2011 ◽  
pp. 1747-1752 ◽  
Author(s):  
Yehuda Lindell
Keyword(s):  
Data Mining ◽  
Privacy Preserving ◽  
Secure Computation ◽  
Data Mining Algorithm ◽  
Privacy Preserving Data Mining ◽  
Public And Private ◽  
The Public ◽  
Use Of Data ◽  
To Come ◽  
Mining Tools

The increasing use of data mining tools in both the public and private sectors raises concerns regarding the potentially sensitive nature of much of the data being mined. The utility to be gained from widespread data mining seems to come into direct conflict with an individual’s need and right to privacy. Privacy preserving data mining solutions achieve the somewhat paradoxical property of enabling a data mining algorithm to use data without ever actually “seeing” it. Thus, the benefits of data mining can be enjoyed, without compromising the privacy of concerned individuals.

A Survey on Privacy Preserving Dynamic Data Publishing

2021 ◽  
pp. 1635-1657
Author(s):  
Salheddine Kabou ◽  
Sidi mohamed Benslimane ◽  
Mhammed Mosteghanemi
Keyword(s):  
Data Privacy ◽  
Privacy Preservation ◽  
Personal Information ◽  
Privacy Preserving ◽  
Data Publishing ◽  
Future Research ◽  
Dynamic Data ◽  
Practical Applications ◽  
New Process ◽  
Future Research Directions

Many organizations, especially small and medium business (SMB) enterprises require the collection and sharing of data containing personal information. The privacy of this data must be preserved before outsourcing to the commercial public. Privacy preserving data publishing PPDP refers to the process of publishing useful information while preserving data privacy. A variety of approaches have been proposed to ensure privacy by applying traditional anonymization models which focused only on the single publication of datasets. In practical applications, data publishing is more complicated where the organizations publish multiple times for different recipients or after modifications to provide up-to-date data. Privacy preserving dynamic data publication PPDDP is a new process in privacy preservation which addresses the anonymization of the data for different purposes. In this survey, the author will systematically evaluate and summarize different studies to PPDDP, clarify the differences and requirements between the scenarios that can exist, and propose future research directions.

scholarly journals Secure Multiparty Computation for Privacy Preserving Data Mining

2011 ◽  
pp. 1005-1009 ◽  
Author(s):  
Yehida Lindell
Keyword(s):  
Data Mining ◽  
Privacy Preserving ◽  
Data Mining Algorithm ◽  
Multiparty Computation ◽  
Privacy Preserving Data Mining ◽  
Public And Private ◽  
The Public ◽  
Use Of Data ◽  
To Come ◽  
Mining Tools

The increasing use of data-mining tools in both the public and private sectors raises concerns regarding the potentially sensitive nature of much of the data being mined. The utility to be gained from widespread data mining seems to come into direct conflict with an individual’s need and right to privacy. Privacy-preserving data-mining solutions achieve the somewhat paradoxical property of enabling a data-mining algorithm to use data without ever actually seeing it. Thus, the benefits of data mining can be enjoyed without compromising the privacy of concerned individuals.

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