Utility Analysis and Enhancement of LDP Mechanisms in High-Dimensional Space. (arXiv:2201.07469v1 [cs.CR])

Local differential privacy (LDP), which perturbs the data of each user
locally and only sends the noisy version of her information to the aggregator,
is a popular privacy-preserving data collection mechanism. In LDP, the data
collector could obtain accurate statistics without access to original data,
thus guaranteeing privacy. However, a primary drawback of LDP is its
disappointing utility in high-dimensional space. Although various LDP schemes
have been proposed to reduce perturbation, they share the same and naive
aggregation mechanism at …

analysis space