1 research outputs found
Continuous Release of Data Streams under both Centralized and Local Differential Privacy
In this paper, we study the problem of publishing a stream of real-valued
data satisfying differential privacy (DP). One major challenge is that the
maximal possible value can be quite large; thus it is necessary to estimate a
threshold so that numbers above it are truncated to reduce the amount of noise
that is required to all the data. The estimation must be done based on the data
in a private fashion. We develop such a method that uses the Exponential
Mechanism with a quality function that approximates well the utility goal while
maintaining a low sensitivity. Given the threshold, we then propose a novel
online hierarchical method and several post-processing techniques.
Building on these ideas, we formalize the steps into a framework for private
publishing of stream data. Our framework consists of three components: a
threshold optimizer that privately estimates the threshold, a perturber that
adds calibrated noises to the stream, and a smoother that improves the result
using post-processing. Within our framework, we design an algorithm satisfying
the more stringent setting of DP called local DP (LDP). To our knowledge, this
is the first LDP algorithm for publishing streaming data. Using four real-world
datasets, we demonstrate that our mechanism outperforms the state-of-the-art by
a factor of 6-10 orders of magnitude in terms of utility (measured by the mean
squared error of answering a random range query)