arXiv subjects

Aleksandra Slavković

Publications and source records attributed to Aleksandra Slavković.

10 recordsLinked to original sources

arXiv2026

Differentially Private Linear Regression and Synthetic Data Generation with Statistical Guarantees

In the social sciences, small- to medium-scale datasets are common, and linear regression is canonical. In privacy-aware settings, much work has focused on differentially private (DP) linear regression, but mostly on point estimation with limited attention to uncertainty quantification. Meanwhile, synthetic data generation (SDG) is increasingly important for reproducibility studies, yet current DP linear regression methods do not readily support it. Mainstream DP-SDG approaches either are tailored to discrete or discretized data, making them less suitable for analyses involving continuous variables, or rely on deep learning models that require large datasets, limiting their use for the smaller-scale data typical in social science. We propose a method for linear regression with valid inference under Gaussian DP. It includes a bias-corrected estimator with asymptotic confidence intervals (CIs) and a general SDG procedure such that the corresponding regression on the synthetic data matches our DP linear regression procedure. Our approach is effective in small- to moderate-dimensional settings. Experiments show that our method (1) improves accuracy over existing methods for DP linear regression, (2) provides valid CIs, and (3) produces more reliable synthetic data for downstream statistical and machine learning tasks than current DP synthesizers.

cs.LG↗

arXiv2026

Statistics-Friendly Confidentiality Protection for Establishment Data, with Applications to the QCEW

Confidentiality for business data is an understudied area of disclosure avoidance, where legacy methods struggle to provide acceptable results. Standard formal privacy techniques for person-level data, like differential privacy, are designed to protect against membership inference and hence do not provide suitable confidentiality/utility trade-offs due to the highly skewed nature of business data and because extreme outlier records are often important contributors to query answers. Prior proposals, therefore, took a personalized differential privacy approach that allowed privacy parameters to degrade for the outlying records -- larger establishments get weaker membership inference guarantees. However, providing guarantees to some entities that are strictly weaker than guarantees for others is problematic from a policy standpoint. In this paper, we propose a novel confidentiality framework for business data with a focus on interpretability for policy makers. Instead of protecting against membership inference, which is often not a concern in business data, we protect against attribute inferences that are too precise. In our framework, data curators specify a neighbor function that is used to define uncertainty interval bands around an establishment's attribute values and the privacy parameters govern the strength of indistinguishability between values within the same uncertainty interval.We propose two query-answering mechanisms under this framework and evaluate them on: (1) a confidential Quarterly Census of Employment and Wages (QCEW) dataset produced by the U.S. Bureau of Labor Statistics (this was done through a cooperative agreement), and (2) a substitute dataset that we created from public sources (and will publicly release).

cs.CR↗

arXiv2026

Assessing Utility of Differential Privacy for RCTs

Randomized controlled trials (RCTs) have become powerful tools for assessing the impact of interventions and policies in many contexts. They are considered the gold standard for causal inference in the biomedical fields and many social sciences. Researchers have published an increasing number of studies that rely on RCTs for at least part of their inference. These studies typically include the response data that has been collected, de-identified, and sometimes protected through traditional disclosure limitation methods. In this paper, we empirically assess the impact of privacy-preserving synthetic data generation methodologies on published RCT analyses by leveraging available replication packages (research compendia) in economics and policy analysis. We implement three privacy-preserving algorithms, that use as a base one of the basic differentially private (DP) algorithms, the perturbed histogram, to support the quality of statistical inference. We highlight challenges with the straight use of this algorithm and the stability-based histogram in our setting and described the adjustments needed. We provide simulation studies and demonstrate that we can replicate the analysis in a published economics article on privacy-protected data under various parameterizations. We find that relatively straightforward (at a high-level) privacy-preserving methods influenced by DP techniques allow for inference-valid protection of published data. 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.

stat.AP↗

arXiv2023

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.

stat.AP↗

arXiv2022

A Latent Class Modeling Approach for Generating Synthetic Data and Making Posterior Inferences from Differentially Private Counts

Several algorithms exist for creating differentially private counts from contingency tables, such as two-way or three-way marginal counts. The resulting noisy counts generally do not correspond to a coherent contingency table, so that some post-processing step is needed if one wants the released counts to correspond to a coherent contingency table. We present a latent class modeling approach for post-processing differentially private marginal counts that can be used (i) to create differentially private synthetic data from the set of marginal counts, and (ii) to enable posterior inferences about the confidential counts. We illustrate the approach using a subset of the 2016 American Community Survey Public Use Microdata Sets and the 2004 National Long Term Care Survey.

stat.ME↗

arXiv2020

Privacy for Spatial Point Process Data

In this work we develop methods for privatizing spatial location data, such as spatial locations of individual disease cases. We propose two novel Bayesian methods for generating synthetic location data based on log-Gaussian Cox processes (LGCPs). We show that conditional predictive ordinate (CPO) estimates can easily be obtained for point process data. We construct a novel risk metric that utilizes CPO estimates to evaluate individual disclosure risks. We adapt the propensity mean square error (pMSE) data utility metric for LGCPs. We demonstrate that our synthesis methods offer an improved risk vs. utility balance in comparison to radial synthesis with a case study of Dr. John Snow's cholera outbreak data.

stat.AP↗

arXiv2019

Benefits and Pitfalls of the Exponential Mechanism with Applications to Hilbert Spaces and Functional PCA

The exponential mechanism is a fundamental tool of Differential Privacy (DP) due to its strong privacy guarantees and flexibility. We study its extension to settings with summaries based on infinite dimensional outputs such as with functional data analysis, shape analysis, and nonparametric statistics. We show that one can design the mechanism with respect to a specific base measure over the output space, such as a Guassian process. We provide a positive result that establishes a Central Limit Theorem for the exponential mechanism quite broadly. We also provide an apparent negative result, showing that the magnitude of the noise introduced for privacy is asymptotically non-negligible relative to the statistical estimation error. We develop an \ep-DP mechanism for functional principal component analysis, applicable in separable Hilbert spaces. We demonstrate its performance via simulations and applications to two datasets.

cs.CR↗

arXiv2018

pMSE Mechanism: Differentially Private Synthetic Data with Maximal Distributional Similarity

We propose a method for the release of differentially private synthetic datasets. In many contexts, data contain sensitive values which cannot be released in their original form in order to protect individuals' privacy. Synthetic data is a protection method that releases alternative values in place of the original ones, and differential privacy (DP) is a formal guarantee for quantifying the privacy loss. We propose a method that maximizes the distributional similarity of the synthetic data relative to the original data using a measure known as the pMSE, while guaranteeing epsilon-differential privacy. Additionally, we relax common DP assumptions concerning the distribution and boundedness of the original data. We prove theoretical results for the privacy guarantee and provide simulations for the empirical failure rate of the theoretical results under typical computational limitations. We also give simulations for the accuracy of linear regression coefficients generated from the synthetic data compared with the accuracy of non-differentially private synthetic data and other differentially private methods. Additionally, our theoretical results extend a prior result for the sensitivity of the Gini Index to include continuous predictors.

stat.ME↗

arXiv2016

Inference using noisy degrees: Differentially private $β$-model and synthetic graphs

The $β$-model of random graphs is an exponential family model with the degree sequence as a sufficient statistic. In this paper, we contribute three key results. First, we characterize conditions that lead to a quadratic time algorithm to check for the existence of MLE of the $β$-model, and show that the MLE never exists for the degree partition $β$-model. Second, motivated by privacy problems with network data, we derive a differentially private estimator of the parameters of $β$-model, and show it is consistent and asymptotically normally distributed - it achieves the same rate of convergence as the nonprivate estimator. We present an efficient algorithm for the private estimator that can be used to release synthetic graphs. Our techniques can also be used to release degree distributions and degree partitions accurately and privately, and to perform inference from noisy degrees arising from contexts other than privacy. We evaluate the proposed estimator on real graphs and compare it with a current algorithm for releasing degree distributions and find that it does significantly better. Finally, our paper addresses shortcomings of current approaches to a fundamental problem of how to perform valid statistical inference from data released by privacy mechanisms, and lays a foundational groundwork on how to achieve optimal and private statistical inference in a principled manner by modeling the privacy mechanism; these principles should be applicable to a class of models beyond the $β$-model.

stat.ME↗

arXiv2014

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 $χ^2$-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).

stat.AP↗