  Local differential privacy (LDP) can be adopted to anonymize richer user data
attributes that will be input to sophisticated machine learning (ML) tasks.
However, today's LDP approaches are largely task-agnostic and often lead to
severe performance loss -- they simply inject noise to all data attributes
according to a given privacy budget, regardless of what features are most
relevant for the ultimate task. In this paper, we address how to significantly
improve the ultimate task performance with multi-dimensional user data by
considering a task-aware privacy preservation problem. The key idea is to use
an encoder-decoder framework to learn (and anonymize) a task-relevant latent
representation of user data. We obtain an analytical near-optimal solution for
the linear setting with mean-squared error (MSE) task loss. We also provide an
approximate solution through a gradient-based learning algorithm for general
nonlinear cases. Extensive experiments demonstrate that our task-aware approach
significantly improves ultimate task accuracy compared to standard benchmark
LDP approaches with the same level of privacy guarantee.
