Multi-party High-Dimensional Related Data Publishing via Probabilistic Principal Component Analysis and Differential Privacy

Gu, Zhen; Zhang, Guoyin; Yang, Chen

doi:10.1007/978-3-030-96791-8_9

Cited by 2 publications

(4 citation statements)

References 24 publications

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“…In order to prevent both parties from leaking private information, the anonymization process satisfies both differential privacy and secure two-party computation. Gu et al [32] presented the PPCA-DP-MH approach. e data owners collaborate with a semitrusted curator to reduce the dimensionality of the data, and then the data owners used the probabilistic generative model of principal component analysis to generate a published data set.…”

Section: Privacy-preserving Data Publishing In Distributedmentioning

confidence: 99%

Horizontally Partitioned Data Publication with Differential Privacy

Zhang

Yang

2022

Security and Communication Networks

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In this paper, we study the privacy-preserving data publishing problem in a distributed environment. The data contain sensitive information; hence, directly pooling and publishing the local data will lead to privacy leaks. To solve this problem, we propose a multiparty horizontally partitioned data publishing method under differential privacy (HPDP-DP). First, in order to make the noise level of the published data in the distributed scenario the same as in the centralized scenario, we use the infinite divisibility of the Laplace distribution to design a distributed noise addition scheme to perturb the locally shared data and use Paillier encryption to transmit the locally shared data to the semitrusted curator. Then, the semitrusted curator obtains the estimator of the covariance matrix of the aggregated data with Laplace noise and then obtains the principal components of the aggregated data and returns them to each data owner. Finally, the data owner utilizes the generative model of probabilistic principal component analysis to generate a synthetic data set for publication. We conducted experiments on different real data sets; the experimental results demonstrate that the synthetic data set released by the HPDP-DP method can maintain high utility.

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Section: Privacy-preserving Data Publishing In Distributedmentioning

confidence: 99%

Horizontally Partitioned Data Publication with Differential Privacy

Zhang

Yang

2022

Security and Communication Networks

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“…is is the first research about differentially private data publishing for arbitrarily partitioned data. In our prior work [16], we proposed the PPCA-DP-MH algorithm. First, data owners and a semitrusted third party cooperated to reduce the dimension of high-dimensional data to obtain the top k principal components that satisfy differential privacy, and then each data owner used the generative model of probabilistic principal component analysis to generate a data set with the same scale as the original data for publication.…”

Section: Privacy Preserving Data Publishing In Distributedmentioning

confidence: 99%

“…First, data owners and a semitrusted third party cooperated to reduce the dimension of high-dimensional data to obtain the top k principal components that satisfy differential privacy, and then each data owner used the generative model of probabilistic principal component analysis to generate a data set with the same scale as the original data for publication. Different from the prior work [16], this paper uses the linear discriminant analysis to publish the projection data with differential privacy. Linear discriminant analysis can retain the class information of the data while reducing the dimension, which is beneficial to maintain the utility of the published data in classification.…”

Section: Privacy Preserving Data Publishing In Distributedmentioning

confidence: 99%

“…Data from a single data owner may not be sufficient for statistical learning, and aggregating data by a single data owner may not be possible. For example [16], in Table 1, the data are possessed by three data owners. Each row in Table 1 represents the information of an individual, where records 1 to 4 are from data owner 1, records 5 to 8 are from data owner 2, and records 9 to 10 are from data owner 3.…”

Section: Introductionmentioning

confidence: 99%

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Multiparty Data Publishing via Blockchain and Differential Privacy

Zhang

2022

Security and Communication Networks

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Data are distributed between different parties. Collecting data from multiple parties for analysis and mining will serve people better. However, it also brings unprecedented privacy threats to the participants. Therefore, safe and reliable data publishing among multiple data owners is an urgent problem to be solved. We mainly study the problem of privacy protection in data publishing. For a centralized scenario, we propose the LDA-DP algorithm. First, the within-class mean vectors and the pooled within-class scatter matrix are perturbed by the Gaussian noise. 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. For distributed scenarios, we propose the Mul-LDA-DP algorithm based on a blockchain and differential privacy technology. First, the within-class mean vectors and within-class scatter matrices of local data are perturbed by the Gaussian noise and uploaded to the blockchain network. Second, the projection direction vector is calculated in the blockchain network and returned to the data owner. 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. Furthermore, in a distributed scenario, we propose a correlated noise generation scheme that uses the additivity of the Gaussian distribution to mitigate the effects of noise and can achieve the same noise level as the centralized scenario. We measure the utility of the published data by the SVM misclassification rate. We conduct comparative experiments with similar algorithms on different real data sets. The experimental results show that the data released by the two algorithms can maintain good utility in SVM classification.

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Multi-party High-Dimensional Related Data Publishing via Probabilistic Principal Component Analysis and Differential Privacy

Cited by 2 publications

References 24 publications

Horizontally Partitioned Data Publication with Differential Privacy

Horizontally Partitioned Data Publication with Differential Privacy

Multiparty Data Publishing via Blockchain and Differential Privacy

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