A New Geometric Data Perturbation Method for Data Anonymization Based on Random Number Generators

Abstract

With the technology’s rapid development and its involvement in all areas of our lives, the volume and value of data have become a significant field of study. Valuation of the data to this extent has produced some consequences in terms of people’s knowledge. Data anonymization is the most important of these issues in terms of the security of personal data. Much work has been done in this area and continues to being done. In this study, we proposed a method called RSUGP for the anonymization of sensitive attributes. A new noise model based on random number generators has been proposed instead of the Gaussian noise or random noise methods, which are being used conventionally in geometric data perturbation. We tested our proposed RSUGP method with six different databases and four different classification methods for classification accuracy and attack resistance; then, we presented the results section. Experiments show that the proposed method was more successful than the other two classification accuracy, attack resistance, and runtime.

Preserving Privacy of IoT Healthcare Data using Differential Privacy and LSTM

“…Et Al. [66] Geometric data perturbation (GDP) methods have proven to be highly successful and Kanmaz has achieved a better privacy by using GDP along with random number generators using static data. The sensitivity of a laplace function provides an upper constraint on the amount that its output needs to be changed for us to maintain our privacy, that is to say.…”