  Conventional private data publication mechanisms aim to retain as much data
utility as possible while ensuring sufficient privacy protection on sensitive
data. Such data publication schemes implicitly assume that all data analysts
and users have the same data access privilege levels. However, it is not
applicable for the scenario that data users often have different levels of
access to the same data, or different requirements of data utility. The
multi-level privacy requirements for different authorization levels pose new
challenges for private data publication. Traditional PPDP mechanisms only
publish one perturbed and private data copy satisfying some privacy guarantee
to provide relatively accurate analysis results. To find a good tradeoff
between privacy preservation level and data utility itself is a hard problem,
let alone achieving multi-level data utility on this basis. In this paper, we
address this challenge in proposing a novel framework of data publication with
compressive sensing supporting multi-level utility-privacy tradeoffs, which
provides differential privacy. Specifically, we resort to compressive sensing
(CS) method to project a $n$-dimensional vector representation of users' data
to a lower $m$-dimensional space, and then add deliberately designed noise to
satisfy differential privacy. Then, we selectively obfuscate the measurement
vector under compressive sensing by adding linearly encoded noise, and provide
different data reconstruction algorithms for users with different authorization
levels. Extensive experimental results demonstrate that ML-DPCS yields
multi-level of data utility for specific users at different authorization
levels.
