arXiv subjects

Anne-Sophie Charest

Publications and source records attributed to Anne-Sophie Charest.

4 recordsLinked to original sources

arXiv2026

Exact association-test distributions for prior-informed stepwise SNP prioritization

Prioritizing causal variants in Genome-Wide Association Studies (GWAS) is challenging due to linkage disequilibrium and high dimensionality. We propose an iterative algorithm for the selection of genetic variables that allows the integration of biological priors. The proposed algorithm requires the specification of the power function of a test statistic and incorporates a priori information on each SNP. We derive the approximate and exact distributions of several commonly used test statistics. At each step of the proposed algorithm, we select the SNP with the largest a posteriori probability of being causal. We consider several stopping criteria for the algorithm. We conduct comprehensive simulation studies to assess the performance of several versions of the proposed algorithm and to make a comparison to some existing competitors. The use of the proposed prioritization algorithm is illustrated with a cis-eQTL application to a human gene-expression data, using European discovery and Yoruba cross-population replication.

stat.ME↗

arXiv2026

Neighborhood Watch: Privacy Risks in Seeded Local Combination Synthetic Data

Synthetic data is seen as a promising solution for sharing data in sensitive contexts. However, recent work on privacy attacks have shown that there are still significant residual risks, especially for synthetic data generations methods that are not based on formal approaches such as differential privacy. In this paper, we investigate the privacy risks associated with local combination approaches for generating synthetic data in which synthetic profiles are built by combining real neighbouring profiles. More precisely, we focus on three methods from this family, namely SMOTE, Simulant and Avatar, which have been recently used as a way to share 'anonymised data' in the healthcare domain. In particular, we conduct an extensive privacy analysis through a diverse set of attacks: membership inference, linkage and reconstruction attacks. Our results demonstrate substantial privacy leakage for all three methods, raising serious doubts about whether their outputs should be regarded as anonymous in practice.

cs.CR↗

arXiv2024

Inference With Combining Rules From Multiple Differentially Private Synthetic Datasets

Differential privacy (DP) has been accepted as a rigorous criterion for measuring the privacy protection offered by random mechanisms used to obtain statistics or, as we will study here, synthetic datasets from confidential data. Methods to generate such datasets are increasingly numerous, using varied tools including Bayesian models, deep neural networks and copulas. However, little is still known about how to properly perform statistical inference with these differentially private synthetic (DIPS) datasets. The challenge is for the analyses to take into account the variability from the synthetic data generation in addition to the usual sampling variability. A similar challenge also occurs when missing data is imputed before analysis, and statisticians have developed appropriate inference procedures for this case, which we tend extended to the case of synthetic datasets for privacy. In this work, we study the applicability of these procedures, based on combining rules, to the analysis of DIPS datasets. Our empirical experiments show that the proposed combining rules may offer accurate inference in certain contexts, but not in all cases.

stat.ME↗

arXiv2016

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.

stat.ME↗