The Privacy Data Protection Model Based on Random Projection Technology

In general, as the technology developed, big data analysis provides opportunities to reduce the implementation time and budget. Compared with traditional methods, big data analysis has a greater advantage. It seeks to establish a privacy framework consistent with the factors and measures of mutual understanding in the context of random projection technology. Privacy concerns and perceived benefits have proven to greatly influence personal data protection. The success of stochastic projection techniques depends on voter privacy and personal data protection needs being met. It explores independent component analysis as a possible tool for breaking privacy with deterministic multiplicative perturbation-based models, for example: random orthogonal transformations and random rotations. An approach based on approximate random projection is then proposed that improves privacy protection while maintaining certain date statistical characteristics.