Linear Queries Estimation with Local Differential Privacy
share
We study the problem of estimating a set of d linear queries with respect to some unknown distribution p over a domain J=[J] based on a sensitive data set of n individuals under the constraint of local differential privacy. This problem subsumes a wide range of estimation tasks, e.g., distribution estimation and d-dimensional mean estimation. We provide new algorithms for both the offline (non-adaptive) and adaptive versions of this problem. In the offline setting, the set of queries are fixed before the algorithm starts. In the regime where n≲ d^2/(J), our algorithms attain L_2 estimation error that is independent of d, and is tight up to a factor of Õ(^1/4(J)). For the special case of distribution estimation, we show that projecting the output estimate of an algorithm due to [Acharya et al. 2018] on the probability simplex yields an L_2 error that depends only sub-logarithmically on J in the regime where n≲ J^2/(J). These results show the possibility of accurate estimation of linear queries in the high-dimensional settings under the L_2 error criterion. In the adaptive setting, the queries are generated over d rounds; one query at a time. In each round, a query can be chosen adaptively based on all the history of previous queries and answers. We give an algorithm for this problem with optimal L_∞ estimation error (worst error in the estimated values for the queries w.r.t. the data distribution). Our bound matches a lower bound on the L_∞ error for the offline version of this problem [Duchi et al. 2013].
READ FULL TEXT
