Preserving Privacy of Individuals in Data Mining Research Paper

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Introduction

There is exponential growth in the amount of data collections that contain person-specific information. The organizations that collect this data are entrusted to ensures that the data remains private and that no external entities have access to the data. However, there are instances that the data can be beneficial to researchers and analysts in their attempts to answer numerous questions. In many cases, organizations would like to share this data while protecting the privacy of the individuals. In an attempt to protect the privacy, it becomes hard for the organization to preserve the utility of the data, which would result in less accurate analytical outcomes (Sweeney, 2002). The data owner would like to have a way that they can transform datasets containing highly sensitive information into privacy-preserving records that they can easily share with other researchers or corporate partners. However, there have been numerous cases of organizations releasing datasets that they believe are anonymized only for the records to be re-identified. Therefore, it is vital for organizations to understand how the anonymizations techniques work and assess how they can be safely applied to datasets. This is where k-anonymity comes into play. K-anonymity is a privacy model that is applied in order to protect the data subjects' privacy when sharing data. A release of data is considered to have k-anonymity property if the data for each individual contained in the release cannot be distinguished from at least one k-1 individuals whose data also appears in the release. K-anonymity reduces the risk of re-identification of any anonymized data by ensuring that any linkages to other datasets are not possible. Using k-anonymity property one is able to make the dataset less precise and ambiguous in some way while preserving its usability for research or other purposes (Fung, Wang, Fu, & Philip, 2010).

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The Article’s Proposed Method/Approach

The article being reviewed is titled “The cost of quality: Implementing generalization and suppression for anonymizing biomedical data with minimal information loss.” The article combines generalization and suppression in order to ensure that there is less likelihood of the dataset records being re-identified (Kohlmayer, Prasser, & Kuhn, 2015). The generalization method replaces individual values of attributes with a broader category thus preventing the re-identification of the individual values. For example, a value ‘19’ that is of the age attribute could be replaced with ‘? 20’. This would anonymize the values for age and make it hard for re-identification to occur. Suppression of values entails the replacement of certain values of the attributes with an asterisk. All or some of the values found in a column could be replaced by the asterisk. For example, the values of the attribute name could be all replaced with an asterisk or some of the values for zip code could be replaced with asterisks.

These two methods have limitations and combining the two methods into one decreases the risk of the data being re-identified. Kohlmayer et al. (2015) posit that combining the two techniques there is the preservation of the truthfulness of the information in the dataset. It is also possible for the dataset to preserve the privacy of the individuals when the two methods are used together. Any information that is left out by one of the methods can be easily eliminated by the other method and this will ensure….....

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