Add to ORKGPrivacy is an important issue in data mining and knowledge discovery. In this paper, we propose to use the randomized response techniques to conduct the data mining computation. Specically, we present a method to build naive Bayesian classiers from the disguised data. We conduct experiments to compare the accuracy of our classier with the one built from the original undisguised data. Our results show that although the data are disguised, our method can still achieve fairly high accuracy. KEY WORDS Privacy, security, naive Bayesian classication, data mining
Abstract. Data perturbation with random noise signals has been shown to be useful for data hiding in...
Data mining is a process in which data collected from different sources is analyzed for useful infor...
Randomization has emerged as an important approach for data disguising in Privacy-Preserving Data Pu...
Privacy preserving data mining is to discover accurate patterns without precise access to the origin...
Privacy is an important issue in data mining and knowledge discovery. In this paper, we propose to u...
Privacy preserving classification is to develop a classifier without precise access to the original ...
Privacy is an important issue in data mining and knowledge discovery. In this paper, we describe a ...
Randomization has emerged as a useful technique for data disguising in privacy-preserving data minin...
Practical Bayesian inference depends upon detailed examination of posterior distribution. When the p...
This paper studies how to enforce differential privacy by using the randomized response in the data ...
We consider the problem of enhancing user privacy in common data analysis and machine learning devel...
Privacy against an adversary (AD) that tries to detect the underlying privacy-sensitive data distrib...
In order to extract interesting patterns, data available at multiple sites has to be trained. The da...
Privacy against an adversary (AD) that tries to detect the underlying privacy-sensitive data distrib...
International audienceDifferential privacy formalises privacy-preserving mechanisms that provide acc...
Abstract. Data perturbation with random noise signals has been shown to be useful for data hiding in...
Data mining is a process in which data collected from different sources is analyzed for useful infor...
Randomization has emerged as an important approach for data disguising in Privacy-Preserving Data Pu...
Privacy preserving data mining is to discover accurate patterns without precise access to the origin...
Privacy is an important issue in data mining and knowledge discovery. In this paper, we propose to u...
Privacy preserving classification is to develop a classifier without precise access to the original ...
Privacy is an important issue in data mining and knowledge discovery. In this paper, we describe a ...
Randomization has emerged as a useful technique for data disguising in privacy-preserving data minin...
Practical Bayesian inference depends upon detailed examination of posterior distribution. When the p...
This paper studies how to enforce differential privacy by using the randomized response in the data ...
We consider the problem of enhancing user privacy in common data analysis and machine learning devel...
Privacy against an adversary (AD) that tries to detect the underlying privacy-sensitive data distrib...
In order to extract interesting patterns, data available at multiple sites has to be trained. The da...
Privacy against an adversary (AD) that tries to detect the underlying privacy-sensitive data distrib...
International audienceDifferential privacy formalises privacy-preserving mechanisms that provide acc...
Abstract. Data perturbation with random noise signals has been shown to be useful for data hiding in...
Data mining is a process in which data collected from different sources is analyzed for useful infor...
Randomization has emerged as an important approach for data disguising in Privacy-Preserving Data Pu...