The Applicability of the Perturbation Based Privacy Preserving Data Mining for Real-World Data
Source: Elsevier B.V.
The perturbation method has been extensively studied for privacy preserving data mining. In this method, random noise from a known distribution is added to the privacy sensitive data before the data is sent to the data miner. Subsequently, the data miner reconstructs an approximation to the original data distribution from the perturbed data and uses the reconstructed distribution for data mining purposes. Due to the addition of noise, loss of information versus preservation of privacy is always a trade off in the perturbation based approaches. The question is, to what extent are the users willing to compromise their privacy? This is a choice that changes from individual to individual. Different individuals may have different attitudes towards privacy based on customs and cultures.