  High-dimensional crowdsourced data collected from a large number of users
produces rich knowledge for our society. However, it also brings unprecedented
privacy threats to participants. Local privacy, a variant of differential
privacy, is proposed as a means to eliminate the privacy concern.
Unfortunately, achieving local privacy on high-dimensional crowdsourced data
raises great challenges on both efficiency and effectiveness. Here, based on EM
and Lasso regression, we propose efficient multi-dimensional joint distribution
estimation algorithms with local privacy. Then, we develop a Locally
privacy-preserving high-dimensional data Publication algorithm, LoPub, by
taking advantage of our distribution estimation techniques. In particular, both
correlations and joint distribution among multiple attributes can be identified
to reduce the dimension of crowdsourced data, thus achieving both efficiency
and effectiveness in locally private high-dimensional data publication.
Extensive experiments on real-world datasets demonstrated that the efficiency
of our multivariate distribution estimation scheme and confirm the
effectiveness of our LoPub scheme in generating approximate datasets with local
privacy.
