32 research outputs found
Understanding Compressive Adversarial Privacy
Designing a data sharing mechanism without sacrificing too much privacy can
be considered as a game between data holders and malicious attackers. This
paper describes a compressive adversarial privacy framework that captures the
trade-off between the data privacy and utility. We characterize the optimal
data releasing mechanism through convex optimization when assuming that both
the data holder and attacker can only modify the data using linear
transformations. We then build a more realistic data releasing mechanism that
can rely on a nonlinear compression model while the attacker uses a neural
network. We demonstrate in a series of empirical applications that this
framework, consisting of compressive adversarial privacy, can preserve
sensitive information
Differentially Private Model Selection with Penalized and Constrained Likelihood
In statistical disclosure control, the goal of data analysis is twofold: The
released information must provide accurate and useful statistics about the
underlying population of interest, while minimizing the potential for an
individual record to be identified. In recent years, the notion of differential
privacy has received much attention in theoretical computer science, machine
learning, and statistics. It provides a rigorous and strong notion of
protection for individuals' sensitive information. A fundamental question is
how to incorporate differential privacy into traditional statistical inference
procedures. In this paper we study model selection in multivariate linear
regression under the constraint of differential privacy. We show that model
selection procedures based on penalized least squares or likelihood can be made
differentially private by a combination of regularization and randomization,
and propose two algorithms to do so. We show that our private procedures are
consistent under essentially the same conditions as the corresponding
non-private procedures. We also find that under differential privacy, the
procedure becomes more sensitive to the tuning parameters. We illustrate and
evaluate our method using simulation studies and two real data examples