A paper on marginal release under the model of local differential privacy has been accepted for presentation at the SIGMOD conference. A related tutorial will also be presented on the model of Local Differential Privacy (LDP). Details and links are as follows:
Marginal release under local differential privacy (Cormode, Kulkarni, Srivastava)
Many analysis and machine learning tasks require the availability of marginal statistics on multidimensional datasets while providing strong privacy guarantees for the data subjects. Applications for these statistics range from finding correlations in the data to fitting sophisticated prediction models. In this paper, we provide a set of algorithms for materializing marginal statistics under the strong model of local differential privacy. We prove the first tight theoretical bounds on the accuracy of marginals compiled under each approach, perform empirical evaluation to confirm these bounds, and evaluate them for tasks such as modeling and correlation testing. Our results show that releasing information based on (local) Fourier transformations of the input is preferable to alternatives based directly on (local) marginals.
Privacy at scale: Local differential privacy in practice (G. Cormode, S. Jha, T. Kulkarni, N. Li, D. Srivastava, and T. Wang). Tutorial at SIGMOD 2018 and KDD 2018
Local differential privacy (LDP), where users randomly perturb their inputs to provide plausible deniability of their data without the need for a trusted party, has been adopted recently by several major technology organizations, including Google, Apple and Microsoft. This tutorial aims to introduce the key technical underpinnings of these deployed systems, to survey current research that addresses related problems within the LDP model, and to identify relevant open problems and research directions for the community.