  Organizations with a large user base, such as Samsung and Google, can
potentially benefit from collecting and mining users' data. However, doing so
raises privacy concerns, and risks accidental privacy breaches with serious
consequences. Local differential privacy (LDP) techniques address this problem
by only collecting randomized answers from each user, with guarantees of
plausible deniability; meanwhile, the aggregator can still build accurate
models and predictors by analyzing large amounts of such randomized data. So
far, existing LDP solutions either have severely restricted functionality, or
focus mainly on theoretical aspects such as asymptotical bounds rather than
practical usability and performance. Motivated by this, we propose Harmony, a
practical, accurate and efficient system for collecting and analyzing data from
smart device users, while satisfying LDP. Harmony applies to multi-dimensional
data containing both numerical and categorical attributes, and supports both
basic statistics (e.g., mean and frequency estimates), and complex machine
learning tasks (e.g., linear regression, logistic regression and SVM
classification). Experiments using real data confirm Harmony's effectiveness.
