关键词:
personalized local differential privacy
mean estimation
crowdsensing model
摘要:
The fast development of the Internet and mobile devices results in a crowdsensing business model,where individuals(users)are willing to contribute their data to help the institution(data collector)analyze and release useful ***,the reveal of personal data will bring huge privacy threats to users,which will impede the wide application of the crowdsensing *** settle the problem,the definition of local differential privacy(LDP)is ***,to respond to the varied privacy preference of users,resear-chers propose a new model,i.e.,personalized local differential privacy(PLDP),which allow users to specify their own privacy *** this paper,we focus on a basic task of calculating the mean value over a single numeric attribute with *** on the previous schemes for mean estimation under LDP,we employ PLDP model to design novel schemes(LAP,DCP,PWP)to provide personalized privacy for each *** then theoretically analysis the worst-case variance of three proposed schemes and conduct experiments on synthetic and real datasets to evaluate the performance of three *** theoretical and experimental results show the optimality of PWP in the low privacy regime and a slight advantage of DCP in the high privacy regime.