Differentially private high dimensional sparse covariance matrix estimation

Wang, Di

doi:10.1016/j.tcs.2021.03.001

Cited by 4 publications

(13 citation statements)

References 7 publications

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“…Specifically, the framework leverages a deep learning-based data minimization model to constrain the data size and obfuscates the learned features from original data by adaptively injecting noise to achieve the LDP, hence constructing a novel privacy preservation layer against sensitive information inference on the edge server. [104] focused on the study of sparse covariance matrix estimation problem under the differential privacy, proposed a novel DP-resholding method to achieve the ℓ 2 -norm error bound, and further extended the bound to a general-norm based one (1 ≤ w ≤ ∞). e method is significantly better than the one adding noise directly and easily extended to the LDP model.…”

Section: Private Federated Learning and Deep Learning In The Local Modelmentioning

confidence: 99%

A Comprehensive Survey on Local Differential Privacy

Xiong

Liu

et al. 2020

Security and Communication Networks

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With the advent of the era of big data, privacy issues have been becoming a hot topic in public. Local differential privacy (LDP) is a state-of-the-art privacy preservation technique that allows to perform big data analysis (e.g., statistical estimation, statistical learning, and data mining) while guaranteeing each individual participant’s privacy. In this paper, we present a comprehensive survey of LDP. We first give an overview on the fundamental knowledge of LDP and its frameworks. We then introduce the mainstream privatization mechanisms and methods in detail from the perspective of frequency oracle and give insights into recent studied on private basic statistical estimation (e.g., frequency estimation and mean estimation) and complex statistical estimation (e.g., multivariate distribution estimation and private estimation over complex data) under LDP. Furthermore, we present current research circumstances on LDP including the private statistical learning/inferencing, private statistical data analysis, privacy amplification techniques for LDP, and some application fields under LDP. Finally, we identify future research directions and open challenges for LDP. This survey can serve as a good reference source for the research of LDP to deal with various privacy-related scenarios to be encountered in practice.

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Section: Private Federated Learning and Deep Learning In The Local Modelmentioning

confidence: 99%

A Comprehensive Survey on Local Differential Privacy

Xiong

Liu

et al. 2020

Security and Communication Networks

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“…We used a very simple algorithm for the differential privacy technique. This section examines what happens when we use more advanced differential privacy techniques, such as [15], [36], [37], [38]. However, the techniques used in this article and more advanced differential privacy techniques can strictly adhere to a given privacy parameter, .…”

Section: Discussionmentioning

confidence: 99%

Re-Identification in Differentially Private Incomplete Datasets

Sei

Okumura

Ohsuga

2022

IEEE Open J. Comput. Soc.

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Efforts to counter COVID-19 reaffirmed the importance of rich medical, behavioral, and sociological data. To make data available to many researchers who can conduct statistical analyses and machine learning, personally identifiable information must be excluded to protect individual privacy. It is essential to remove explicit identifiers, sample population data, and apply differential privacy, the de facto standard privacy metric. Despite the general belief that the risk of re-identification is insignificant when these techniques are applied, this study shows that even after applying these techniques, the risk of being re-identified is highly significant for some data. This study proposes in detail an algorithm for estimating the number of people in a population who have certain attribute values based on incomplete, differentially private databases. If the estimated number is one, the probability that only one person with that attribute value is present in the population is high, which means that there is a high probability of re-identification. Therefore, this study concludes that the re-identification risk must be evaluated even after applying state-of-the-art techniques to protect privacy.INDEX TERMS Differential privacy, ethical and privacy framework, re-identification.

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“…Moreover, the distribution assumption is required to be Gaussian. Recently, Wang and Xu [10] reduced the distribution assumption from Gaussian to sub-Gaussian and imposed the sparse structure on the covariance matrix, which led to a low convergence rate, while using a DPA and LDPA to preserve privacy.…”

Section: Related Work and Our Contributionsmentioning

confidence: 99%

“…The DPA and LDPA aim at hiding the true information while keeping the basic property of the whole dataset. A popular idea to achieve this goal is to add some special noise into the original model [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Differentially Private Sparse Covariance Matrix Estimation under Lower-Bounded Moment Assumption

Li,

Wang

2023

Mathematics

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This paper investigates the problem of sparse covariance matrix estimation while the sampling set contains sensitive information, and both the differentially private algorithm and locally differentially private algorithm are adopted to preserve privacy. It is worth noting that the requirement of the distribution assumption in our work is only the existing bounded 4+ε(ε>0) moment. Meanwhile, we reduce the error bounds by modifying the threshold of the existing differentially private algorithms. Finally, the numerical simulations and results from a real data application are presented to support our theoretical claims.

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Differentially private high dimensional sparse covariance matrix estimation

Cited by 4 publications

References 7 publications

A Comprehensive Survey on Local Differential Privacy

A Comprehensive Survey on Local Differential Privacy

Re-Identification in Differentially Private Incomplete Datasets

Differentially Private Sparse Covariance Matrix Estimation under Lower-Bounded Moment Assumption

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