Fusion: Privacy-Preserving Distributed Protocol for High-Dimensional Data Mashup

2015 ◽  
Author(s):  
Gaby G. Dagher ◽  
Farkhund Iqbal ◽  
Mahtab Arafati ◽  
Benjamin C. M. Fung
Keyword(s):  
High Dimensional Data ◽  
Privacy Preserving ◽  
High Dimensional ◽  
Distributed Protocol ◽  
Data Mashup

Privacy-preserving high-dimensional data publishing for classification

2020 ◽  
Vol 93 ◽  
pp. 101785
Author(s):  
Rong Wang ◽  
Yan Zhu ◽  
Chin-Chen Chang ◽  
Qiang Peng
Keyword(s):  
High Dimensional Data ◽  
Privacy Preserving ◽  
Data Publishing ◽  
High Dimensional

scholarly journals Privacy-Preserving High-dimensional Data Collection with Federated Generative Autoencoder

2021 ◽  
Vol 2022 (1) ◽  
pp. 481-500
Author(s):  
Xue Jiang ◽  
Xuebing Zhou ◽  
Jens Grossklags
Keyword(s):  
Machine Learning ◽  
Data Collection ◽  
Private Information ◽  
Differential Privacy ◽  
High Dimensional Data ◽  
Synthetic Data ◽  
Privacy Preserving ◽  
High Dimensional ◽  
Data Utility ◽  
High Utility

Abstract Business intelligence and AI services often involve the collection of copious amounts of multidimensional personal data. Since these data usually contain sensitive information of individuals, the direct collection can lead to privacy violations. Local differential privacy (LDP) is currently considered a state-ofthe-art solution for privacy-preserving data collection. However, existing LDP algorithms are not applicable to high-dimensional data; not only because of the increase in computation and communication cost, but also poor data utility. In this paper, we aim at addressing the curse-of-dimensionality problem in LDP-based high-dimensional data collection. Based on the idea of machine learning and data synthesis, we propose DP-Fed-Wae, an efficient privacy-preserving framework for collecting high-dimensional categorical data. With the combination of a generative autoencoder, federated learning, and differential privacy, our framework is capable of privately learning the statistical distributions of local data and generating high utility synthetic data on the server side without revealing users’ private information. We have evaluated the framework in terms of data utility and privacy protection on a number of real-world datasets containing 68–124 classification attributes. We show that our framework outperforms the LDP-based baseline algorithms in capturing joint distributions and correlations of attributes and generating high-utility synthetic data. With a local privacy guarantee ∈ = 8, the machine learning models trained with the synthetic data generated by the baseline algorithm cause an accuracy loss of 10% ~ 30%, whereas the accuracy loss is significantly reduced to less than 3% and at best even less than 1% with our framework. Extensive experimental results demonstrate the capability and efficiency of our framework in synthesizing high-dimensional data while striking a satisfactory utility-privacy balance.

scholarly journals Privacy-Preserving Data Sharing in High Dimensional Regression and Classification Settings

2012 ◽  
Vol 4 (1) ◽  
Author(s):  
Stephen E. Fienberg ◽  
Jiashun Jin
Keyword(s):  
Phase Diagram ◽  
Phase Space ◽  
Data Sharing ◽  
High Dimensional Data ◽  
Privacy Preserving ◽  
High Dimensional ◽  
Statistical Research ◽  
Higher Criticism ◽  
Dimensional Phase Space ◽  
Privacy Violation

We focus on the problem of multi-party data sharing in high dimensional data settings where the number of measured features (or the dimension) p is frequently much larger than the number of subjects (or the sample size) n, the so-called p >> n scenario that has been the focus of much recent statistical research. Here, we consider data sharing for two interconnected problems in high dimensional data analysis, namely the feature selection and classification. We characterize the notions of ``cautious", ``regular", and ``generous" data sharing in terms of their privacy-preserving implications for the parties and their share of data, with focus on the ``feature privacy" rather than the ``sample privacy", though the violation of the former may lead to the latter. We evaluate the data sharing methods using {\it phase diagram} from the statistical literature on multiplicity and Higher Criticism thresholding. In the two-dimensional phase space calibrated by the signal sparsity and signal strength, a phase diagram is a partition of the phase space and contains three distinguished regions, where we have no (feature)-privacy violation, relatively rare privacy violations, and an overwhelming amount of privacy violation.

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