Studies on the Use of Various Noise Strategies for Perturbing Data in Privacy-Preserving Data Mining
Keywords:
Privacy Preserving, Data Mining, Data perturbation, Maximum A Posteriori, Principal Component Analysis (PCA)Abstract
Privacy Preserving Data Mining techniques broadly fall under the categories of randomization and value distortion. Randomization replaces the existing value with a non existing value whereas value distortion modifies each value in the database. Data perturbation is widely used randomization technique that promises both valid data mining result and secured privacy. Existing research works were conducted with additive and multiplicative forms of data perturbation. Privacy is measured with the increasing level of error rate based on different types of attacks. Attacks in data perturbation are normally noise filtering methods which are based on linear and nonlinear filtering schemes. Linear filtering process produces the output data which will be in linear combination of the input values. The perturbed copies in additive and multiplicative data perturbation is generated with Laplace noiseWhen the input data is in linear distribution and is subjected to perturbation, linear filtering schemes are used to reconstruct the data and analyze the privacy measure. In nonlinear filtering schemes, the output of the filtering process will not be in linear distribution. This means that for a non linear data distribution, linear noise filtering schemes cannot be used for accurate analysis. Attacks considered so far are based on only linear filtering schemes. The perturbed models are evaluated with both privacy and utility of data mining. The work describes both linear and non linear type of attacks over the generated perturbed data. Attacks for additive data perturbation include Maximum A Posteriori (MAP) and Principal Component Analysis (PCA) based filtering method.
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