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The Journal of Privacy and Confidentiality
Scopus
ISSN 2575-8527 · Labor Dynamics Institute · 231 artikel di indeks
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Artikel Jurnal
Sublinear Space Graph Algorithms in the Continual Release Model
The graph continual release model of differential privacy seeks to produce differentially private solutions to graph problems under a stream of edge updates where new private solutions are released after each update. Previously known edge differentially private algorithms for most graph problems…
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Prior Distributions for Gaussian Models Under Differential Privacy with Bounds on Data Values
The Gaussian distribution is often used to model univariate, numerical data, even in contexts where individuals' data values are bounded, e.g., test scores and biological measurements. We consider Bayesian inference for the parameters of Gaussian models when (i) analysts seek a post-processing…
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Maximizing Utility for Vector-weighted Pseudo Posterior Mechanisms under Differential Privacy
The risk-weighted pseudo posterior mechanism provides a practical framework for privacy protection that takes advantage of the availability of posterior sampling approaches, creating a synthesizer for microdata dissemination. The flexibility of the approach lies in the user-specification of the…
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Corrigendum to "Differentially Private Set Union"
In the published version of "Differentially Private Set Union" by Gopi et. al, the authors identified an error in Proposition 5.1. This error affects Proposition 5.1 and Algorithm 8 of the published version.
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Slowly Scaling Per-Record Differential Privacy
We develop formal privacy mechanisms for releasing statistics from data with many outlying values, such as income data. These mechanisms ensure that a per-record differential privacy guarantee degrades slowly in the protected records’ influence on the statistics being released. Records with greater…
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Concurrent Composition for Interactive Differential Privacy with Adaptive Privacy-Loss Parameters
In this paper, we study the concurrent composition of interactive mechanisms with adaptively chosen privacy-loss parameters. In this setting, the adversary can interleave queries to existing interactive mechanisms, as well as create new ones. We prove that every valid privacy filter and odometer…
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Tight Bounds for Machine Unlearning via Differential Privacy
We consider the formulation of ``machine unlearning'' of Sekhari, Acharya, Kamath, and Suresh (NeurIPS 2021), which formalizes the so-called ``"right to be forgotten" by requiring that a trained model, upon request, should be able to 'unlearn' a number of points from the training data, as if they…
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Synthetic Census Data Generation via Multidimensional Multiset Sum
The US Decennial Census provides valuable data for both research and policy purposes. Census data are subject to a variety of disclosure avoidance techniques prior to release in order to preserve respondent confidentiality. While many are interested in studying the impacts of disclosure avoidance…
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Bayesian Methods to Improve The Accuracy of Differentially Private Measurements of Constrained Parameters
Formal disclosure avoidance techniques are necessary to ensure that published data can not be used to identify information about individuals. The addition of statistical noise to unpublished data can be implemented to achieve {\em differential privacy}, which provides a formal mathematical privacy…
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The Risk of Linked Census Data to Transgender Youth
Every ten years, the United States Census Bureau collects data on all people living in the US, including information on age, sex, race, ethnicity, and household relationship. We conducted a simulation study to investigate the risk of disclosing a change in how an individual's sex was recorded in…
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Private Stochastic Optimization with Large Worst-Case Lipschitz Parameter
We study differentially private (DP) stochastic optimization (SO) with loss functions whose worst-case Lipschitz parameter over all data points may be huge or infinite. To date, the most work on DP SO assumes that the loss is uniformly Lipschitz continuous over data (i.e. stochastic gradients are…
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Rethinking Disclosure Prevention with Pointwise Maximal Leakage
This paper introduces a paradigm shift in the way privacy is defined, driven by a novel interpretation of the fundamental result of Dwork and Naor about the impossibility of absolute disclosure prevention. We propose a general model of utility and privacy in which utility is achieved by disclosing…
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Differentially Private Image Classification by Learning Priors from Random Processes
In privacy-preserving machine learning, differentially private stochastic gradient descent (DP-SGD) performs worse than SGD due to per-sample gradient clipping and noise addition. A recent focus in private learning research is improving the performance of DP-SGD on private data by incorporating…
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Avoiding Floating-Point Side Channels in the Report Noisy Max with Gap Mechanism
The Noisy Max mechanism and its variations are fundamental private selection algorithms that are used to select items from a set of candidates (such as the most common diseases in a population), while controlling the privacy leakage in the underlying data. A recently proposed extension, Noisy Top-k…
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Achieving Privacy Utility Balance for Multivariate Time Series Data
Utility-preserving data privatization is of utmost importance for data-producing agencies. The popular noise-addition privacy mechanism distorts autocorrelation patterns in time series data, thereby marring utility; in response, [21] introduced all-pass filtering (FLIP) as a utility-preserving time…
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Bridging the Privacy Accounting Gap in DP-SGD
Differentially Private Stochastic Gradient Descent (DP-SGD) is one of the most widely used algorithms for private machine learning. Due to its efficiency, most practical implementations of DP-SGD shuffle the training examples and divide them into fixed-size mini-batches during training. However…
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Differentially Private Synthetic Control
Synthetic control is a causal inference tool used to estimate the treatment effects of an intervention by creating synthetic counterfactual data. This approach combines measurements from other similar observations (i.e., donor pool) to predict a counterfactual time series of interest (i.e., target…
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On the Connection Between the ABS Perturbation Methodology and Differential Privacy
This paper explores analytical connections between the perturbation methodology of the Australian Bureau of Statistics (ABS) and the differential privacy (DP) framework. We consider a single static counting query function and find the analytical form of the perturbation distribution with symmetric…
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Launching the Society for Privacy and Confidentiality Research to Own the Journal of Privacy and Confidentiality
We describe the launching of the Society for Privacy and Confidentiality Research (SPCR). SPCR is the new owner of the Journal of Privacy and Confidentiality, with the goal of ensuring a sustainable future for the Journal, and continuing to publish the multidisciplinary, open access journal that…