Differential Privacy for Clinical Trial Data: Preliminary Evaluations

Vu, Duy; Slavković, Aleksandra

doi:10.1109/icdmw.2009.52

Cited by 83 publications

(80 citation statements)

References 17 publications

(19 reference statements)

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“…Our results are similar to those reported in the current literature on Laplace mechanism for noise addition to histograms or smaller contingency tables with proportions (e.g., [3], [7]). Instead we focus on the release of differentially-private χ 2 -statistics, p-values and the most relevant SNPs.…”

Section: Resultssupporting

confidence: 82%

“…Thus for increasing N , the perturbed χ 2 -statistics get more accurate. For related simulations that demonstrate the interactive effect of sample size and privacy level and compare asymptotic efficiency of private and non-private estimators for 2×2 tables and the corresponding χ 2 -statistics, see [7]. We can perform a similar analysis on the p-values corresponding to the χ 2 -statistics assuming a χ 2 -distribution with 2 degrees of freedom as null distribution, cf.…”

Section: Nmentioning

confidence: 99%

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Privacy Preserving GWAS Data Sharing

Fienberg

Slavković

Uhler

2011

2011 IEEE 11th International Conference on Data Mining Workshops

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Abstract-Traditional statistical methods for the confidentiality protection for statistical databases do not scale well to deal with GWAS (genome-wide association studies) databases and external information on them. The more recent concept of differential privacy, introduced by the cryptographic community, is an approach which provides a rigorous definition of privacy with meaningful privacy guarantees in the presence of arbitrary external information. Building on such notions, we propose new methods to release aggregate GWAS data without compromising an individual's privacy. We present methods for releasing differentially private minor allele frequencies, chisquare statistics and p-values. We compare these approaches on simulated data and on a GWAS study of canine hair length involving 685 dogs. We also propose a privacy-preserving method for finding genome-wide associations based on a differentially private approach to penalized logistic regression.

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Section: Resultssupporting

confidence: 82%

Section: Nmentioning

confidence: 99%

Privacy Preserving GWAS Data Sharing

Fienberg

Slavković

Uhler

2011

2011 IEEE 11th International Conference on Data Mining Workshops

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“…Furthermore, as we show in Section 7, their mechanism often produces highly inaccurate outputs. Vu and Slavković [31] also consider applying differential privacy to medical research data, but focus on determining the population size needed to provide a given power to statistical tests when noise is added using standard mechanisms.…”

Section: Related Workmentioning

confidence: 99%

Privacy-preserving data exploration in genome-wide association studies

Johnson

Shmatikov

2013

Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

156

190

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Genome-wide association studies (GWAS) have become a popular method for analyzing sets of DNA sequences in order to discover the genetic basis of disease. Unfortunately, statistics published as the result of GWAS can be used to identify individuals participating in the study. To prevent privacy breaches, even previously published results have been removed from public databases, impeding researchers’ access to the data and hindering collaborative research. Existing techniques for privacy-preserving GWAS focus on answering specific questions, such as correlations between a given pair of SNPs (DNA sequence variations). This does not fit the typical GWAS process, where the analyst may not know in advance which SNPs to consider and which statistical tests to use, how many SNPs are significant for a given dataset, etc. We present a set of practical, privacy-preserving data mining algorithms for GWAS datasets. Our framework supports exploratory data analysis, where the analyst does not know a priori how many and which SNPs to consider. We develop privacy-preserving algorithms for computing the number and location of SNPs that are significantly associated with the disease, the significance of any statistical test between a given SNP and the disease, any measure of correlation between SNPs, and the block structure of correlations. We evaluate our algorithms on real-world datasets and demonstrate that they produce significantly more accurate results than prior techniques while guaranteeing differential privacy.

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“…Our results are the first to achieve UMP tests under ( , δ)−DP, and are among the first steps towards a general theory of optimal inference under DP. Vu and Slavković (2009) were the first to perform classical hypothesis tests under DP. They develop private tests for population proportions as well as for independence in 2 × 2 contingency tables.…”

Section: Introductionmentioning

confidence: 99%

Differentially Private Inference for Binomial Data

Awan

Slavković

2020

JPC

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We derive uniformly most powerful (UMP) tests for simple and one-sided hypotheses for a population proportion within the framework of Differential Privacy (DP), optimizing finite sample performance. We show that in general, DP hypothesis tests can be written in terms of linear constraints, and for exchangeable data can always be expressed as a function of the empirical distribution. Using this structure, we prove a 'Neyman-Pearson lemma' for binomial data under DP, where the DP-UMP only depends on the sample sum. Our tests can also be stated as a post-processing of a random variable, whose distribution we coin "Truncated-Uniform-Laplace" (Tulap), a generalization of the Staircase and discrete Laplace distributions. Furthermore, we obtain exact p-values, which are easily computed in terms of the Tulap random variable.Using the above techniques, we show that our tests can be applied to give uniformly most accurate one-sided confidence intervals and optimal confidence distributions. We also derive uniformly most powerful unbiased (UMPU) two-sided tests, which lead to uniformly most accurate unbiased (UMAU) two-sided confidence intervals. We show that our results can be applied to distribution-free hypothesis tests for continuous data. Our simulation results demonstrate that all our tests have exact type I error, and are more powerful than current techniques.

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Differential Privacy for Clinical Trial Data: Preliminary Evaluations

Cited by 83 publications

References 17 publications

Privacy Preserving GWAS Data Sharing

Privacy Preserving GWAS Data Sharing

Privacy-preserving data exploration in genome-wide association studies

Differentially Private Inference for Binomial Data

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