A high-dimensional numeric data collection algorithm for local difference privacy based on random projection - Huizhong SUN; Jianyu YANG; Xiang CHENG; Sen SU (大数据)
The problem of high-dimensional data collection satisfying local differential privacy was studied.A new locally differentially private algorithm called Multi-RPHM was proposed based on the random projection technology,which achieved the high utility of the collected high-dimensional numeric data while satisfying the local differential privacy.The algorithm was formally proved to meet ε-local differential privacy.The effectiveness of the algorithm was comfirmed through experiments on synthetic datasets.