Correlated network data publication via differential privacy

Chen, Rui; Fung, Benjamin C. M.; Yu, Philip S.; Desai, Bipin C.

doi:10.1007/s00778-013-0344-8

Cited by 178 publications

(142 citation statements)

References 38 publications

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“…Here the Baseline algorithm adopts the multiplication in [23] to handle with the correlated tuples in a network dataset. Considering the constraint of applying Pearson correlation coefficient, we do not adopt the method using Pearson correlation coefficient as comparison reference in the following experiments.…”

Section: Methodsmentioning

confidence: 99%

“…Similarly, the authors in [22] proposed Bayesian DP to evaluate the level of private information leakage even when data is correlated and prior knowledge is incomplete. The work in [23] regarded the correlation among tuples as complete correlation and multiplied the query sensitivity with the number of correlated tuples in publishing correlated network data, which leaves room for fine-grained correlation analysis in the following work. Aiming to decrease the noise amount, Zhu et al [24] depicted the correlation between tuples via Pearson correlation coefficient, including complete correlation, partial correlation, and independence.…”

Section: Related Workmentioning

confidence: 99%

“…Different from the existing methods of correlation analysis, for example, simple multiplication in [23], Pearson correlation coefficient in [24], and the maximal information coefficient in [31], we extract the PF vectors of edges in a weighted graph corresponding to a correlated dataset and then define the correlation of both edges via JS-Divergence, which is more accurate and reasonable. Specifically, the work in [23] assumes both tuples are completely correlated; however, our proposed correlation results lie in interval [0, 1] representing multiple correlation including complete correlation. In addition, the work in [24] assumes sample data follows normal distribution, while our method is not the case.…”

Section: Related Workmentioning

confidence: 99%

“…Motivated by the entropy in information theory, we adopt JS-Divergence, inferred from KullbackLeibler Divergence (KL-Divergence) [23], to depict the difference of two distributions, which can be transformed to depict the correlation of two tuples in a correlated dataset.…”

Section: Correlation Metricmentioning

confidence: 99%

“…(ii) Similar to [23][24][25], we introduce the correlated parameter representing that there are at most correlated tuples in a dataset. In other words, a tuple is correlated with at most − 1 other tuples; that is, an edge in a graph is correlated with at most − 1 other edges.…”

Section: Security and Communication Networkmentioning

confidence: 99%

See 4 more Smart Citations

An Edge Correlation Based Differentially Private Network Data Release Method

Cai

Wang

et al. 2017

Security and Communication Networks

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Differential privacy (DP) provides a rigorous and provable privacy guarantee and assumes adversaries' arbitrary background knowledge, which makes it distinct from prior work in privacy preserving. However, DP cannot achieve claimed privacy guarantees over datasets with correlated tuples. Aiming to protect whether two individuals have a close relationship in a correlated dataset corresponding to a weighted network, we propose a differentially private network data release method, based on edge correlation, to gain the tradeoff between privacy and utility. Specifically, we first extracted the Edge Profile (PF) of an edge from a graph, which is transformed from a raw correlated dataset. Then, edge correlation is defined based on the PFs of both edges via JensonShannon Divergence (JS-Divergence). Secondly, we transform a raw weighted dataset into an indicated dataset by adopting a weight threshold, to satisfy specific real need and decrease query sensitivity. Furthermore, we propose -correlated edge differential privacy (CEDP), by combining the correlation analysis and the correlated parameter with traditional DP. Finally, we propose network data release (NDR) algorithm based on the -CEDP model and discuss its privacy and utility. Extensive experiments over real and synthetic network datasets show the proposed releasing method provides better utilities while maintaining privacy guarantee.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Correlation Metricmentioning

confidence: 99%

Section: Security and Communication Networkmentioning

confidence: 99%

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An Edge Correlation Based Differentially Private Network Data Release Method

Cai

Wang

et al. 2017

Security and Communication Networks

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Correlated data in differential privacy: Definition and analysis

Zhang

Zhu

Liu

et al. 2020

Concurrency and Computation

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Summary Differential privacy is a rigorous mathematical framework for evaluating and protecting data privacy. In most existing studies, there is a vulnerable assumption that records in a dataset are independent when differential privacy is applied. However, in real‐world datasets, records are likely to be correlated, which may lead to unexpected data leakage. In this survey, we investigate the issue of privacy loss due to data correlation under differential privacy models. Roughly, we classify existing literature into three lines: (1) using parameters to describe data correlation in differential privacy, (2) using models to describe data correlation in differential privacy, and (3) describing data correlation based on the framework of Pufferfish. First, a detailed example is given to illustrate the issue of privacy leakage on correlated data in real scenes. Then our main work is to analyze and compare these methods, and evaluate situations that these diverse studies are applied. Finally, we propose some future challenges on correlated differential privacy.

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MobilityMirror: Bias-Adjusted Transportation Datasets

Rodriguez

Salimi

Ping

et al. 2019

Communications in Computer and Information Science

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We describe customized synthetic datasets for publishing mobility data. Companies are providing new transportation modalities, and their data is of high value for integrative transportation research, policy enforcement, and public accountability. However, these companies are disincentivized from sharing data not only to protect the privacy of individuals (drivers and/or passengers), but also to protect their own competitive advantage. Moreover, demographic biases arising from how the services are delivered may be amplified if released data is used in other contexts. We describe a model and algorithm for releasing origin-destination histograms that removes selected biases in the data using causality-based methods. We compute the origin-destination histogram of the original dataset then adjust the counts to remove undesirable causal relationships that can lead to discrimination or violate contractual obligations with data owners. We evaluate the utility of the algorithm on real data from a dockless bike share program in Seattle and taxi data in New York, and show that these adjusted transportation datasets can retain utility while removing bias in the underlying data.

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Correlated network data publication via differential privacy

Cited by 178 publications

References 38 publications

An Edge Correlation Based Differentially Private Network Data Release Method

An Edge Correlation Based Differentially Private Network Data Release Method

Correlated data in differential privacy: Definition and analysis

MobilityMirror: Bias-Adjusted Transportation Datasets

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