Summary
Differential privacy (DP) has been widely accepted as the de facto technique for protecting data privacy. Despite the decade-long research efforts on DP, there still exists a critical research problem that has been largely overlooked, that is all existing DP studies are grounded on the hypothesis that software can easily and faithfully sample and add noises from a probability distribution. However, this hypothesis is being constantly challenged by recent findings about its privacy violation and by the growing demand of privacy protection in low-end devices that may lack high-level software lib
