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DP2-Pub: Differentially Private High-Dimensional Data Publication with Invariant Post Randomization
[article]
2022
arXiv
pre-print
Preserved Fulltext
Privacy-preserving data publishing has received considerable attention in recent years. Unfortunately, the differentially private publication of high dimensional data remains a challenging problem. ...
In this paper, we propose a differentially private high-dimensional data publication mechanism (DP2-Pub) that runs in two phases: a Markov-blanket-based attribute clustering phase and an invariant post ...
However, the differentially private publication of high dimensional data remains a challenging problem -it suffers from the "Curse of High-Dimensionality" [8] , that is, when the dimensionality increases ...
arXiv:2208.11693v1
fatcat:sbg6kx4vfnfffdsphg4t7mg2lm
NeuroMixGDP: A Neural Collapse-Inspired Random Mixup for Private Data Release
[article]
2023
arXiv
pre-print
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2022 pre-print arXiv:2202.06467v1 fatcat:y23tsjzx25b4ldvuhvwuo4axsi
Mixup of raw data provides a new way of data augmentation, which can help improve utility. However, its performance drastically deteriorates when differential privacy (DP) noise is added. ...
We propose a scheme to mixup the Neural Collapse features to exploit the ETF simplex structure and release noisy mixed features to enhance the utility of the released data. ...
Here we only focus on DP data release algorithms for high dimensional data, which are related to DPFMix. ...
arXiv:2202.06467v2
fatcat:lqabz7btrfen7feficlket73je
HDPView: Differentially Private Materialized View for Exploring High Dimensional Relational Data
[article]
2022
arXiv
pre-print
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query), applicability to high-dimensional data, and space efficiency. ...
How can we explore the unknown properties of high-dimensional sensitive relational data while preserving privacy? ...
How can we explore the properties of high-dimensional sensitive data while preserving privacy? This paper focuses on guaranteeing differential privacy (DP) [15, 16] via random noise injections. ...
arXiv:2203.06791v2
fatcat:24yczflhzzctpahgsrfnhmpmey
Multiparty Data Publishing via Blockchain and Differential Privacy
2022
Security and Communication Networks
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Second, the optimal projection direction vector with differential privacy is obtained by the Fisher criterion. Finally, the low-dimensional projection data of the original data are obtained. ...
Finally, the data owner uses the projection direction vector to generate low-dimensional projection data of the original data and upload it to the blockchain network for publishing. ...
information, and then they released low-dimensional subspaces of high-dimensional sparse data. ...
doi:10.1155/2022/5612794
fatcat:7uypzuz3kvbjxexodovl4aa3zm
Differentially Private High-Dimensional Data Publication via Markov Network
2019
EAI Endorsed Transactions on Security and Safety
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However, it faces some challenges in differentially private high-dimensional data publication, such as the complex attribute relationships, the high computational complexity and data sparsity. ...
Differentially private data publication has recently received considerable attention. ...
[16] proposed DPPro that publishes high-dimensional data via random projection to maximize utility while guaranteeing privacy. Ren et al. ...
doi:10.4108/eai.29-7-2019.159626
fatcat:uakxu2jf2vgnlp2nnwmh6cc63q
Winning the NIST Contest: A scalable and general approach to differentially private synthetic data
2021
Journal of Privacy and Confidentiality
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Central to this approach is Private-PGM, a post-processing method that is used to estimate a high-dimensional data distribution from noisy measurements of its marginals. ...
We propose a general approach for differentially private synthetic data generation, that consists of three steps: (1) select a collection of low-dimensional marginals, (2) measure those marginals with ...
Dppro: Differentially private high-dimensional data release
via random projection. ...
doi:10.29012/jpc.778
fatcat:b2s37gulojbxxm2buyrfzw7vq4
Design of a Privacy-Preserving Data Platform for Collaboration Against Human Trafficking
[article]
2020
arXiv
pre-print
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might be linked to known individuals or groups; (2) aggregate data mitigating the utility risk that synthetic data might misrepresent statistics needed for official reporting; and (3) visual analytics ...
We present new methods to anonymize, publish, and explore such data, implemented as a pipeline generating three artifacts: (1) synthetic data mitigating the privacy risk that published attribute combinations ...
Under looser constraints, DPPro [61] uses random projections that maintain probabilistic (ϵ, δ )differential privacy [14] . ...
arXiv:2005.05688v2
fatcat:ep72y2ehnfdfdepffrlikfccaa