Nprivacy-enhancing k-anonymization of customer data pdf files

A popular approach for data anonymization is kanonymity. Data anonymity, streaming data, crime reporting, privacy enhancing model, kanonymity, information. Pdf efficient kanonymization using clustering techniques. There is increasing pressure to share health information and even make it. Datasets for replication of all the experiments and results text files. Data utility verses privacy has to do with how useful a published data set is to a consumer of that. Applying data privacy techniques on tabular data in uganda arxiv. In this paper, we provide privacy enhancing methods for creating k anonymous tables in a distributed scenario. Our solutions enhance the privacy of kanonymization in the distributed scenario by maintaining endtoend privacy from the original customer data to the final kanonymous results. Enhancing privacy of confidential data using k anonymization. Protecting privacy using kanonymity journal of the american. Pdf data deidentification reconciles the demand for release of data for research purposes and the demand for privacy from individuals. A kanonymized data set has the property that each record is similar to at.

Our solutions are presented in sections 4 and 5, respectively. In section 3, we formalize our two problem formulations. Any record in a k anonymized data set has a maximum probability an external file that. We give two different formulations of this problem, with provably private solutions. In order to protect individuals privacy, the technique of k anonymization has been proposed to deassociate sensitive attributes from the corresponding identifiers. There is increasing pressure to share health information and even make. In short, anonymization algorithms masking methods transform a data file x. Unesco chair in data privacy, department of computer.

Adaptive buffer resizing for efficient anonymization of. Data privacy, kanonymity, ldiversity, privacy preserving data publishing. The technique of kanonymization has been proposed in the literature as an alternative way to release public information, while ensuring both data privacy and data integrity. Data privacy has been studied in the area of statistics statistical. The baseline k anonymity model, which represents current practice, would work well for protecting against the prosecutor reidentification scenario. A look at documents from authorities that govern communication technology in uganda, the. Specifically, we consider a setting in which there is a set of customers, each of whom has a row of a table, and a miner. Analysis of the kimwinkler algorithm for masking microdata files how. Privacyenhancing kanonymization of customer data core. However, our empirical results show that the baseline k anonymity model is very conservative in terms of reidentification risk under the journalist reidentification scenario. Supplementary materials for how to avoid reidentification. Each customer encrypts her sensitive attributes using an encryption key that can be derived by the miner if and only if there are.