arXiv subjects

Jayshree Sarathy

Publications and source records attributed to Jayshree Sarathy.

11 recordsLinked to original sources

arXiv2026

Differential Perspectives: Epistemic Disconnects Surrounding the US Census Bureau's Use of Differential Privacy

When the U.S. Census Bureau announced its intention to modernize its disclosure avoidance procedures for the 2020 Census, it sparked a controversy that is still underway. The move to differential privacy introduced technical and procedural uncertainties, leaving stakeholders unable to evaluate the quality of the data. More importantly, this transformation exposed the statistical illusions and limitations of census data, weakening stakeholders' trust in the data and in the Census Bureau itself. This essay examines the epistemic currents of this controversy. Drawing on theories from Science and Technology Studies (STS) and ethnographic fieldwork, we analyze the current controversy over differential privacy as a battle over uncertainty, trust, and legitimacy of the Census. We argue that rebuilding trust will require more than technical repairs or improved communication; it will require reconstructing what we identify as a 'statistical imaginary.'

cs.CY↗

arXiv2026

Statistical Imaginaries, State Legitimacy: Grappling with the Arrangements Underpinning Quantification in the U.S. Census

Over the last century, the adoption of novel scientific methods for conducting the U.S. census has been met with wide-ranging receptions. Some methods were quietly embraced, while others sparked decades-long controversies. What accounts for these differences? We argue that controversies emerge from $\textit{arrangements of statistical imaginaries}$, putting into tension divergent visions of the census. To analyze these dynamics, we compare reactions to two methods designed to improve data accuracy (imputation and adjustment) and two methods designed to protect confidentiality (swapping and differential privacy), offering insight into how each method reconfigures stakeholder orientations and rhetorical claims. These cases allow us to reflect on how technocratic efforts to improve accuracy and confidentiality can strengthen -- or erode -- trust in data. Our analysis shows how the credibility of the Census Bureau and its data stem not just from empirical evaluations of quantification, but also from how statistical imaginaries are contested and stabilized.

cs.CY↗

arXiv2025

"Having Confidence in My Confidence Intervals": How Data Users Engage with Privacy-Protected Wikipedia Data

In response to calls for open data and growing privacy threats, organizations are increasingly adopting privacy-preserving techniques such as differential privacy (DP) that inject statistical noise when generating published datasets. These techniques are designed to protect privacy of data subjects while enabling useful analyses, but their reception by data users is under-explored. We developed documentation that presents the noise characteristics of two Wikipedia pageview datasets: one using rounding (heuristic privacy) and another using DP (formal privacy). After incorporating expert feedback (n=5), we used these documents to conduct a task-based contextual inquiry (n=15) exploring how data users--largely unfamiliar with these methods--perceive, interact with, and interpret privacy-preserving noise during data analysis. Participants readily used simple uncertainty metrics from the documentation, but struggled when asked to compute confidence intervals across multiple noisy estimates. They were better able to devise simulation-based approaches for computing uncertainty with DP data compared to rounded data. Surprisingly, several participants incorrectly believed DP's stronger utility implied weaker privacy protections. Based on our findings, we offer design recommendations for documentation and tools to better support data users working with privacy-noised data.

cs.HC↗

arXiv2024

"I inherently just trust that it works": Investigating Mental Models of Open-Source Libraries for Differential Privacy

Differential privacy (DP) is a promising framework for privacy-preserving data science, but recent studies have exposed challenges in bringing this theoretical framework for privacy into practice. These tensions are particularly salient in the context of open-source software libraries for DP data analysis, which are emerging tools to help data stewards and analysts build privacy-preserving data pipelines for their applications. While there has been significant investment into such libraries, we need further inquiry into the role of these libraries in promoting understanding of and trust in DP, and in turn, the ways in which design of these open-source libraries can shed light on the challenges of creating trustworthy data infrastructures in practice. In this study, we use qualitative methods and mental models approaches to analyze the differences between conceptual models used to design open-source DP libraries and mental models of DP held by users. Through a two-stage study design involving formative interviews with 5 developers of open-source DP libraries and user studies with 17 data analysts, we find that DP libraries often struggle to bridge the gaps between developer and user mental models. In particular, we highlight the tension DP libraries face in maintaining rigorous DP implementations and facilitating user interaction. We conclude by offering practical recommendations for further development of DP libraries.

cs.HC↗

arXiv2024

Centering Policy and Practice: Research Gaps around Usable Differential Privacy

As a mathematically rigorous framework that has amassed a rich theoretical literature, differential privacy is considered by many experts to be the gold standard for privacy-preserving data analysis. Others argue that while differential privacy is a clean formulation in theory, it poses significant challenges in practice. Both perspectives are, in our view, valid and important. To bridge the gaps between differential privacy's promises and its real-world usability, researchers and practitioners must work together to advance policy and practice of this technology. In this paper, we outline pressing open questions towards building usable differential privacy and offer recommendations for the field, such as developing risk frameworks to align with user needs, tailoring communications for different stakeholders, modeling the impact of privacy-loss parameters, investing in effective user interfaces, and facilitating algorithmic and procedural audits of differential privacy systems.

cs.CR↗

arXiv2024

ATTAXONOMY: Unpacking Differential Privacy Guarantees Against Practical Adversaries

Differential Privacy (DP) is a mathematical framework that is increasingly deployed to mitigate privacy risks associated with machine learning and statistical analyses. Despite the growing adoption of DP, its technical privacy parameters do not lend themselves to an intelligible description of the real-world privacy risks associated with that deployment: the guarantee that most naturally follows from the DP definition is protection against membership inference by an adversary who knows all but one data record and has unlimited auxiliary knowledge. In many settings, this adversary is far too strong to inform how to set real-world privacy parameters. One approach for contextualizing privacy parameters is via defining and measuring the success of technical attacks, but doing so requires a systematic categorization of the relevant attack space. In this work, we offer a detailed taxonomy of attacks, showing the various dimensions of attacks and highlighting that many real-world settings have been understudied. Our taxonomy provides a roadmap for analyzing real-world deployments and developing theoretical bounds for more informative privacy attacks. We operationalize our taxonomy by using it to analyze a real-world case study, the Israeli Ministry of Health's recent release of a birth dataset using DP, showing how the taxonomy enables fine-grained threat modeling and provides insight towards making informed privacy parameter choices. Finally, we leverage the taxonomy towards defining a more realistic attack than previously considered in the literature, namely a distributional reconstruction attack: we generalize Balle et al.'s notion of reconstruction robustness to a less-informed adversary with distributional uncertainty, and extend the worst-case guarantees of DP to this average-case setting.

cs.CR↗

arXiv2024

Analyzing the Differentially Private Theil-Sen Estimator for Simple Linear Regression

In this paper, we study differentially private point and confidence interval estimators for simple linear regression. Motivated by recent work that highlights the strong empirical performance of an algorithm based on robust statistics, DPTheilSen, we provide a rigorous, finite-sample analysis of its privacy and accuracy properties, offer guidance on setting hyperparameters, and show how to produce differentially private confidence intervals to accompany its point estimates.

cs.CR↗

arXiv2023

Controlling Privacy Loss in Sampling Schemes: an Analysis of Stratified and Cluster Sampling

Sampling schemes are fundamental tools in statistics, survey design, and algorithm design. A fundamental result in differential privacy is that a differentially private mechanism run on a simple random sample of a population provides stronger privacy guarantees than the same algorithm run on the entire population. However, in practice, sampling designs are often more complex than the simple, data-independent sampling schemes that are addressed in prior work. In this work, we extend the study of privacy amplification results to more complex, data-dependent sampling schemes. We find that not only do these sampling schemes often fail to amplify privacy, they can actually result in privacy degradation. We analyze the privacy implications of the pervasive cluster sampling and stratified sampling paradigms, as well as provide some insight into the study of more general sampling designs.

stat.ME↗

arXiv2023

Don't Look at the Data! How Differential Privacy Reconfigures the Practices of Data Science

Across academia, government, and industry, data stewards are facing increasing pressure to make datasets more openly accessible for researchers while also protecting the privacy of data subjects. Differential privacy (DP) is one promising way to offer privacy along with open access, but further inquiry is needed into the tensions between DP and data science. In this study, we conduct interviews with 19 data practitioners who are non-experts in DP as they use a DP data analysis prototype to release privacy-preserving statistics about sensitive data, in order to understand perceptions, challenges, and opportunities around using DP. We find that while DP is promising for providing wider access to sensitive datasets, it also introduces challenges into every stage of the data science workflow. We identify ethics and governance questions that arise when socializing data scientists around new privacy constraints and offer suggestions to better integrate DP and data science.

cs.HC↗

arXiv2021

Non-parametric Differentially Private Confidence Intervals for the Median

Differential privacy is a restriction on data processing algorithms that provides strong confidentiality guarantees for individual records in the data. However, research on proper statistical inference, that is, research on properly quantifying the uncertainty of the (noisy) sample estimate regarding the true value in the population, is currently still limited. This paper proposes and evaluates several strategies to compute valid differentially private confidence intervals for the median. Instead of computing a differentially private point estimate and deriving its uncertainty, we directly estimate the interval bounds and discuss why this approach is superior if ensuring privacy is important. We also illustrate that addressing both sources of uncertainty--the error from sampling and the error from protecting the output--simultaneously should be preferred over simpler approaches that incorporate the uncertainty in a sequential fashion. We evaluate the performance of the different algorithms under various parameter settings in extensive simulation studies and demonstrate how the findings could be applied in practical settings using data from the 1940 Decennial Census.

cs.CR↗

arXiv2020

Differentially Private Simple Linear Regression

Economics and social science research often require analyzing datasets of sensitive personal information at fine granularity, with models fit to small subsets of the data. Unfortunately, such fine-grained analysis can easily reveal sensitive individual information. We study algorithms for simple linear regression that satisfy differential privacy, a constraint which guarantees that an algorithm's output reveals little about any individual input data record, even to an attacker with arbitrary side information about the dataset. We consider the design of differentially private algorithms for simple linear regression for small datasets, with tens to hundreds of datapoints, which is a particularly challenging regime for differential privacy. Focusing on a particular application to small-area analysis in economics research, we study the performance of a spectrum of algorithms we adapt to the setting. We identify key factors that affect their performance, showing through a range of experiments that algorithms based on robust estimators (in particular, the Theil-Sen estimator) perform well on the smallest datasets, but that other more standard algorithms do better as the dataset size increases.

cs.LG↗