28 research outputs found
Scalable Privacy-Preserving Data Sharing Methodology for Genome-Wide Association Studies
The protection of privacy of individual-level information in genome-wide
association study (GWAS) databases has been a major concern of researchers
following the publication of "an attack" on GWAS data by Homer et al. (2008)
Traditional statistical methods for confidentiality and privacy protection of
statistical databases do not scale well to deal with GWAS data, especially in
terms of guarantees regarding protection from linkage to external information.
The more recent concept of differential privacy, introduced by the
cryptographic community, is an approach that provides a rigorous definition of
privacy with meaningful privacy guarantees in the presence of arbitrary
external information, although the guarantees may come at a serious price in
terms of data utility. Building on such notions, Uhler et al. (2013) proposed
new methods to release aggregate GWAS data without compromising an individual's
privacy. We extend the methods developed in Uhler et al. (2013) for releasing
differentially-private -statistics by allowing for arbitrary number of
cases and controls, and for releasing differentially-private allelic test
statistics. We also provide a new interpretation by assuming the controls' data
are known, which is a realistic assumption because some GWAS use publicly
available data as controls. We assess the performance of the proposed methods
through a risk-utility analysis on a real data set consisting of DNA samples
collected by the Wellcome Trust Case Control Consortium and compare the methods
with the differentially-private release mechanism proposed by Johnson and
Shmatikov (2013).Comment: 28 pages, 2 figures, source code available upon reques
Differentially Private Synthetic Heavy-tailed Data
The U.S. Census Longitudinal Business Database (LBD) product contains
employment and payroll information of all U.S. establishments and firms dating
back to 1976 and is an invaluable resource for economic research. However, the
sensitive information in LBD requires confidentiality measures that the U.S.
Census in part addressed by releasing a synthetic version (SynLBD) of the data
to protect firms' privacy while ensuring its usability for research activities,
but without provable privacy guarantees. In this paper, we propose using the
framework of differential privacy (DP) that offers strong provable privacy
protection against arbitrary adversaries to generate synthetic heavy-tailed
data with a formal privacy guarantee while preserving high levels of utility.
We propose using the K-Norm Gradient Mechanism (KNG) with quantile regression
for DP synthetic data generation. The proposed methodology offers the
flexibility of the well-known exponential mechanism while adding less noise. We
propose implementing KNG in a stepwise and sandwich order, such that new
quantile estimation relies on previously sampled quantiles, to more efficiently
use the privacy-loss budget. Generating synthetic heavy-tailed data with a
formal privacy guarantee while preserving high levels of utility is a
challenging problem for data curators and researchers. However, we show that
the proposed methods can achieve better data utility relative to the original
KNG at the same privacy-loss budget through a simulation study and an
application to the Synthetic Longitudinal Business Database
Causal inference in transportation safety studies: Comparison of potential outcomes and causal diagrams
The research questions that motivate transportation safety studies are causal
in nature. Safety researchers typically use observational data to answer such
questions, but often without appropriate causal inference methodology. The
field of causal inference presents several modeling frameworks for probing
empirical data to assess causal relations. This paper focuses on exploring the
applicability of two such modeling frameworks---Causal Diagrams and Potential
Outcomes---for a specific transportation safety problem. The causal effects of
pavement marking retroreflectivity on safety of a road segment were estimated.
More specifically, the results based on three different implementations of
these frameworks on a real data set were compared: Inverse Propensity Score
Weighting with regression adjustment and Propensity Score Matching with
regression adjustment versus Causal Bayesian Network. The effect of increased
pavement marking retroreflectivity was generally found to reduce the
probability of target nighttime crashes. However, we found that the magnitude
of the causal effects estimated are sensitive to the method used and to the
assumptions being violated.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS440 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Assessing Utility of Differential Privacy for RCTs
Randomized control trials, RCTs, have become a powerful tool for assessing
the impact of interventions and policies in many contexts. They are considered
the gold-standard for inference in the biomedical fields and in many social
sciences. Researchers have published an increasing number of studies that rely
on RCTs for at least part of the inference, and these studies typically include
the response data collected, de-identified and sometimes protected through
traditional disclosure limitation methods. In this paper, we empirically assess
the impact of strong privacy-preservation methodology (with \ac{DP}
guarantees), on published analyses from RCTs, leveraging the availability of
replication packages (research compendia) in economics and policy analysis. We
provide simulations studies and demonstrate how we can replicate the analysis
in a published economics article on privacy-protected data under various
parametrizations. We find that relatively straightforward DP-based methods
allow for inference-valid protection of the published data, though
computational issues may limit more complex analyses from using these methods.
The results have applicability to researchers wishing to share RCT data,
especially in the context of low- and middle-income countries, with strong
privacy protection.Comment: Submitte