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.

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.

Distributed Privacy Preserving Clustering via Homomorphic Secret Sharing and Its Application to (Vertically) Partitioned Spatio-Temporal Data

Cyber Crime ◽  
2013 ◽  
pp. 395-415 ◽  
Author(s):  
Can Brochmann Yildizli ◽  
Thomas Pedersen ◽  
Yucel Saygin ◽  
Erkay Savas ◽  
Albert Levi
Keyword(s):  
Data Mining ◽  
Real World ◽  
Privacy Preserving ◽  
Secure Multiparty Computation ◽  
Multiparty Computation ◽  
Privacy Preserving Data Mining ◽  
Computational Costs ◽  
Data Mining Algorithms ◽  
Spatio Temporal ◽  
Mining Algorithms

Recent concerns about privacy issues have motivated data mining researchers to develop methods for performing data mining while preserving the privacy of individuals. One approach to develop privacy preserving data mining algorithms is secure multiparty computation, which allows for privacy preserving data mining algorithms that do not trade accuracy for privacy. However, earlier methods suffer from very high communication and computational costs, making them infeasible to use in any real world scenario. Moreover, these algorithms have strict assumptions on the involved parties, assuming involved parties will not collude with each other. In this paper, the authors propose a new secure multiparty computation based k-means clustering algorithm that is both secure and efficient enough to be used in a real world scenario. Experiments based on realistic scenarios reveal that this protocol has lower communication costs and significantly lower computational costs.

scholarly journals A NOVEL PRIVACY PRESERVING DATA MINING ALGORITHM

2011 ◽  
Vol 11 (ASAT CONFERENCE) ◽  
pp. 1-17
Author(s):  
Fahmy Aly ◽  
Fakhry Medhat ◽  
M. Hanafy ◽  
EI-Zeweidy Aly
Keyword(s):  
Data Mining ◽  
Privacy Preserving ◽  
Data Mining Algorithm ◽  
Privacy Preserving Data Mining ◽  
Mining Algorithm

Distributed Privacy Preserving Clustering via Homomorphic Secret Sharing and its Application to (Vertically) Partitioned Spatio-Temporal Data

2013 ◽  
pp. 45-65
Author(s):  
Can Brochmann Yildizli ◽  
Thomas Pedersen ◽  
Yucel Saygin ◽  
Erkay Savas ◽  
Albert Levi
Keyword(s):  
Data Mining ◽  
Real World ◽  
Privacy Preserving ◽  
Secure Multiparty Computation ◽  
Multiparty Computation ◽  
Privacy Preserving Data Mining ◽  
Computational Costs ◽  
Data Mining Algorithms ◽  
Spatio Temporal ◽  
Mining Algorithms

Recent concerns about privacy issues have motivated data mining researchers to develop methods for performing data mining while preserving the privacy of individuals. One approach to develop privacy preserving data mining algorithms is secure multiparty computation, which allows for privacy preserving data mining algorithms that do not trade accuracy for privacy. However, earlier methods suffer from very high communication and computational costs, making them infeasible to use in any real world scenario. Moreover, these algorithms have strict assumptions on the involved parties, assuming involved parties will not collude with each other. In this paper, the authors propose a new secure multiparty computation based k-means clustering algorithm that is both secure and efficient enough to be used in a real world scenario. Experiments based on realistic scenarios reveal that this protocol has lower communication costs and significantly lower computational costs.

Web based Privacy Disclosure Threats and Control Techniques

2016 ◽  
pp. 334-341
Author(s):  
Tithi Hunka ◽  
Sital Dash ◽  
Prasant Kumar Pattnaik
Keyword(s):  
Data Mining ◽  
Privacy Preserving ◽  
Extensive Study ◽  
Security And Privacy ◽  
Sensitive Data ◽  
Privacy Preserving Data Mining ◽  
Web Based ◽  
Data Mining Techniques ◽  
The Public ◽  
And Control

Due to advancement of internet technologies, web based applications are gaining popularity day by day. Many organizations maintain large volumes of web site based data about individuals that may carry information that cannot be revealed to the public or researchers. While web-based applications are becoming increasingly pervasive by nature, they also present new security and privacy challenges. However, privacy threats effects negatively on sensitive data and possibly leads to the leakage of confidential information. More ever, privacy preserving data mining techniques allow us to protect the sensitive data before it gets published to the public by changing the original micro-data format and contents. This chapter is intended to undertake an extensive study on some ramified disclosure threats to the privacy and PPDM (privacy preserving data mining) techniques as a unified solution to protect against threats.

Sign in / Sign up

Export Citation Format

Share Document