Privacy protection in data mining: A perturbation approach for categorical data

Privacy protection in data mining: A perturbation approach for categorical data

0.00 Avg rating0 Votes
Article ID: iaor20071402
Country: United States
Volume: 17
Issue: 3
Start Page Number: 254
End Page Number: 270
Publication Date: Sep 2006
Journal: Information Systems Research
Authors: ,
Keywords: programming: linear, statistics: multivariate
Abstract:

To respond to growing concerns about privacy of personal information, organizations that use their customers' records in data-mining activities are forced to take actions to protect the privacy of the individuals involved. A common practice for many organizations today is to remove identity-related attributes from the customer records before releasing them to data miners or analysts. We investigate the effect of this practice and demonstrate that many records in a data set could be uniquely identified even after identity-related attributes are removed. We propose a perturbation method for categorical data that can be used by organizations to prevent or limit disclosure of confidential data for identifiable records when the data are provided to analysts for classification, a common data-mining task. The proposed method attempts to preserve the statistical properties of the data based on privacy protection parameters specified by the organization. We show that the problem can be solved in two phases, with a linear programming formulation in Phase I (to preserve the first-order marginal distribution), followed by a simple Bayes-based swapping procedure in Phase II (to preserve the joint distribution). Experiments conducted on several real-world data sets demonstrate the effectiveness of the proposed method.

Reviews

Required fields are marked *. Your email address will not be published.