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检索条件"任意字段=International Conference on Privacy in Statistical Databases, PSD 2012"

共 130 条记录，以下是41-50 订阅

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How Adversarial Assumptions Influence Re-identification Risk Measures: A COVID-19 Case Study

How Adversarial Assumptions Influence Re-identification Risk...

引用

international conference on privacy in statistical databases (psd)

作者： Zhang, Xinmeng Wan, Zhiyu Yan, Chao Brown, J. Thomas Xia, Weiyi Gkoulalas-Divanis, Aris Kantarcioglu, Murat Malin, Bradley Vanderbilt Univ Dept Comp Sci Nashville TN 37235 USA Vanderbilt Univ Med Ctr Dept Biomed Informat Nashville TN USA IBM Watson Hlth Cambridge MA USA Univ Texas Dallas Dept Comp Sci Dallas TX USA Vanderbilt Univ Med Ctr Dept Biostat Nashville TN USA

ISBN: (纸本)9783031139451;9783031139444

The COVID-19 pandemic highlights the need for broad dissemination of case surveillance data. Local and global public health agencies have initiated efforts to do so, but there remains limited data available, due in part to concerns over privacy. As a result, current COVID-19 case surveillance data sharing policies are based on strong adversarial assumptions, such as the expectation that an attacker can readily re-identify individuals based on their distinguishability in a dataset. There are various re-identification risk measures to account for adversarial capabilities;however, the current array insufficiently accounts for real world data challenges - particularly issues of missing records in resources of identifiable records that adversaries may rely upon to execute attacks (e.g., 10 50-year-old male in the de-identified dataset vs. 5 50-year-old male in the identified dataset). In this paper, we introduce several approaches to amend such risk measures and assess re-identification risk in light of how an attacker's capabilities relate to missing records. We demonstrate the potential for these measures through a record linkage attack using COVID-19 case surveillance data and voter registration records in the state of Florida. Our findings demonstrate that adversarial assumptions, as realized in a riskmeasure, can dramatically affect re-identification risk estimation. Notably, we show that the re-identification risk is likely to be substantially smaller than the typical risk thresholds, which suggests that more detailed data could be shared publicly than is currently the case.

关键词： Data sharing Re-identification risk COVID-19 Health data Data privacy

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Plausible Deniability

Plausible Deniability

引用

international conference on privacy in statistical databases (psd)

作者： Sidi, David Bambauer, Jane Univ Arizona Tucson AZ 85712 USA

ISBN: (纸本)9783030575205;9783030575212

From the perspective of responsible data release, simulation is a useful tool for estimating risk from adversaries with an unknown amount of identified auxiliary information. We present a simple approach to simulation of attack on sampled datasets, along with an implementation, and demonstrate how a data steward might make use of it to evaluate the privacy risk of release for data gathered about students in the University of California system.

关键词： statistical disclosure control Simulation Education data

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Evaluating Quality of statistical Disclosure Control Methods - VIOLAS Framework

Evaluating Quality of Statistical Disclosure Control Methods...

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international conference on privacy in statistical databases (psd)

作者： Dziegielewska, Olga Mil Univ Technol Gen Sylwestra Kaliskiego 2 PL-00908 Warsaw Poland

ISBN: (纸本)9783030575205;9783030575212

VIOLAS Framework (Volatile Index of Liable Accuracy for statistical Disclosure Controls) defines an assessment methodology for evaluating quality of statistical disclosure control methods by measuring the key factors determining the effectiveness of such methods. This paper describes the base model and explains how the framework may be used and extended by additional measures that may be desirable in particular IT environments or for specific data sets.

关键词： SDC quality framework VIOLAS framework Risk assessment

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Analysis of Differentially-Private Microdata Using SIMEX

Analysis of Differentially-Private Microdata Using SIMEX

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international conference on privacy in statistical databases (psd)

作者： Charest, Anne-Sophie Nombo, Leila Univ Laval Quebec City PQ Canada

ISBN: (纸本)9783030575205;9783030575212

We are concerned here with the publication of microdata which preserve the confidentiality of the respondents, as measured by differential privacy. We borrow the SIMEX methodology from the measurement error literature to analyze microdata perturbed with the Laplace mechanism, hoping to recover unbiased estimates of the parameters of interest. Our simulations show that the strategy works in theory, but only for values of epsilon too large of be of used in practice. It might however be of use in the case of normal noise addition, when one does not require differential privacy.

关键词： Differential privacy Microdata SIMEX

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Explaining Recurrent Machine Learning Models: Integral privacy Revisited

Explaining Recurrent Machine Learning Models: Integral Priva...

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international conference on privacy in statistical databases (psd)

作者： Torra, Vicenc Navarro-Arribas, Guillermo Galvan, Edgar Umea Univ Dept Comp Sci Umea Sweden Skovde Univ Sch Informat Skovde Sweden Univ Autonoma Barcelona Dept Informat & Commun Engn CYBERCAT Bellaterra Catalonia Spain Maynooth Univ Dept Comp Sci Naturally Inspired Computat Res Grp Maynooth Kildare Ireland

ISBN: (纸本)9783030575205;9783030575212

We have recently introduced a privacy model for statistical and machine learning models called integral privacy. A model extracted from a database or, in general, the output of a function satisfies integral privacy when the number of generators of this model is sufficiently large and diverse. In this paper we show how the maximal c-consensus meets problem can be used to study the databases that generate an integrally private solution. We also introduce a definition of integral privacy based on minimal sets in terms of this maximal c-consensus meets problem.

关键词： Integral privacy Maximal c-consensus meets Clustering Parameter selection

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statistical Disclosure Control When Publishing on Thematic Maps

Statistical Disclosure Control When Publishing on Thematic M...

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international conference on privacy in statistical databases (psd)

作者： Hut, Douwe Goseling, Jasper Van Lieshout, Marie-Colette De Wolfe, Peter-Paul de Jonge, Edwin Univ Twente Enschede Netherlands Stat Netherlands The Hague Netherlands Ctr Wiskunde & Informat Amsterdam Netherlands

ISBN: (纸本)9783030575205;9783030575212

The spatial distribution of a variable, such as the energy consumption per company, is usually plotted by colouring regions of the study area according to an underlying table which is already protected from disclosing sensitive information. The result is often heavily influenced by the shape and size of the regions. In this paper, we are interested in producing a continuous plot of the variable directly from microdata and we protect it by adding random noise. We consider a simple attacker scenario and develop an appropriate sensitivity rule that can be used to determine the amount of noise needed to protect the plot from disclosing private information.

关键词： Energy utilization

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privacy Analysis of Query-Set-Size Control

Privacy Analysis of Query-Set-Size Control

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international conference on privacy in statistical databases (psd)

作者： Nussbaum, Eyal Segal, Michael Ben Gurion Univ Negev Sch Elect & Comp Engn Commun Syst Engn Dept IL-84105 Beer Sheva Israel

ISBN: (纸本)9783030575205;9783030575212

Vast amounts of information of all types are collected daily about people by governments, corporations and individuals. The information is collected, for example, when users register to or use on-line applications, receive health related services, use their mobile phones, utilize search engines, or perform common daily activities. As a result, there is an enormous quantity of privately-owned records that describe individuals' finances, interests, activities, and demographics. These records often include sensitive data and may violate the privacy of the users if published. The common approach to safeguarding user information is to limit access to the data by using an authentication and authorization protocol. However, in many cases the publication of user data for statistical analysis and research can be extremely beneficial for both academic and commercial uses, such as statistical research and recommendation systems. To maintain user privacy when such a publication occurs many databases employ anonymization techniques, either on the query results or the data itself. In this paper we examine and analyze the privacy offered for aggregate queries over a data structures representing linear topologies. Additionally, we offer a privacy probability measure, indicating the probability of an attacker to obtain information defined as sensitive by utilizing legitimate queries over such a system.

关键词： privacy Datasets Anonymity Vehicular network Linear topology privacy measure

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A Partitioned Recoding Scheme for privacy Preserving Data Publishing

A Partitioned Recoding Scheme for Privacy Preserving Data Pu...

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international conference on privacy in statistical databases (psd)

作者： Clifton, Chris Hanson, Eric J. Merrill, Keith Merrill, Shawn Zahraa, Amjad Purdue Univ Dept Comp Sci W Lafayette IN 47907 USA Purdue Univ CERIAS W Lafayette IN 47907 USA Brandeis Univ Dept Math Waltham MA 02453 USA

ISBN: (纸本)9783030575205;9783030575212

There is growing interest in Differential privacy as a disclosure limitation mechanism for statistical data. The increased attention has brought to light a number of subtleties in the definition and mechanisms. We explore an interesting dichotomy in parallel composition, where a subtle difference in the definition of a "neighboring database" leads to significantly different results. We show that by "pre-partitioning" the data randomly into disjoint subsets, then applying well-known anonymization schemes to those pieces, we can eliminate this dichotomy. This provides potential operational benefits, with some interesting implications that give further insight into existing privacy schemes. We explore the theoretical limits of the privacy impacts of pre-partitioning, in the process illuminating some subtle distinctions in privacy definitions. We also discuss the resulting utility, including empirical evaluation of the impact on released privatized statistics.

关键词： privacy-preserving techniques

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Advantages of Imputation vs. Data Swapping for statistical Disclosure Control

Advantages of Imputation vs. Data Swapping for Statistical D...

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international conference on privacy in statistical databases (psd)

作者： Kinney, Satkartar K. Looby, Charlotte B. Yu, Feng RTI Int Res Triangle Pk NC 27709 USA

ISBN: (纸本)9783030575205;9783030575212

Data swapping is an approach long-used by public agencies to protect respondent confidentiality in which values of some variables are swapped with similar records for a small portion of respondents. Synthetic data is a newer method in which many if not all values are replaced with multiple imputations. Synthetic data can be difficult to implement for complex data;however, when the portion of data replaced is similar to data swapping, it becomes simple to implement using publicly available software. This paper describes how this simplification of synthetic data can be used to provide a better balance of data quality and disclosure protection compared to data swapping. This is illustrated via an empirical comparison using data from the Survey of Earned Doctorates.

关键词： Synthetic data Applications Methodology

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Utility-Enhancing Flexible Mechanisms for Differential privacy

Utility-Enhancing Flexible Mechanisms for Differential Priva...

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international conference on privacy in statistical databases (psd)

作者： Mugunthan, Vaikkunth Xiao, Wanyi Kagal, Lalana MIT 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (纸本)9783030575205;9783030575212

Differential privacy is a mathematical technique that provides strong theoretical privacy guarantees by ensuring the statistical indistinguishability of individuals in a dataset. It has become the de facto framework for providing privacy-preserving data analysis over statistical datasets. Differential privacy has garnered significant attention from researchers and privacy experts due to its strong privacy guarantees. However, the accuracy loss caused by the noise added has been an issue. First, we propose a new noise adding mechanism that preserves (epsilon, delta)-differential privacy. The distribution pertaining to this mechanism can be observed as a generalized truncated Laplacian distribution. We show that the proposed mechanism adds optimal noise in a global context, conditional upon technical lemmas. We also show that the generalized truncated Laplacian mechanism performs better than the optimal Gaussian mechanism. In addition, we also propose an (epsilon)-differentially private mechanism to improve the utility of differential privacy by fusing multiple Laplace distributions. We also derive the closed-form expressions for absolute expectation and variance of noise for the proposed mechanisms. Finally, we empirically evaluate the performance of the proposed mechanisms and show an increase in all utility measures considered, while preserving privacy.

关键词： Differential privacy Generalized truncated Laplacian distribution Merging of Laplacian distributions

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