Abstract:

To address the limitations of current privacy protection schemes,particularly the insufficient research on data perturbation and the inadequate integration between privacy measurement and privacy protection,we propose a Dynamic Balanced Privacy Model for Data Disturbance(DBPM-DD).First,based on users' privacy preferences and data quality,we design a dynamic measurement mechanism for real-time evaluation of data privacy to precisely measure the amount of private information contained in the data.Second,we propose a data reconstruction mechanism based on probability partitioning and accordingly provide a privacy measurement method for perturbed data,achieving multi-paradigm adaptation of private data.Finally,we introduce a noise scale adaptive adjustment method that adaptively adjusts the noise intensity based on feedback from privacy measurement results,ensuring user privacy while maximizing data utility,thereby achieving a dynamic balance between privacy protection and data utility.Experimental results show that under different noise scales,data sizes,and attack intensities,the model can effectively enhance the degree of privacy protection while maintaining high data utility,providing consistent and effective privacy guarantee under various conditions,and outperforming other privacy protection models.This research provides a new theoretical support for privacy protection in data perturbation technologies,possessing a significant practical application value and a wide range of applicability.

Key words: privacy measurement, privacy-preserving, perturbation techniques, data sharing