Privacy-preserving source separation for distributed data using independent component analysis

Imtiaz, Hafiz; Silva, Rogers F.; Baker, Bradley T.; Plis, Sergey M.; Sarwate, Anand D.; Calhoun, Vince D.

doi:10.1109/ciss.2016.7460488

Cited by 8 publications

(13 citation statements)

References 17 publications

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“…This paper proposes new privacy-preserving algorithms for distributed PCA and OTD and builds upon our earlier work on distributed differentially private eigenvector calculations [17] and centralized differentially private OTD [21]. It improves on our preliminary works on distributed private PCA [17,22] in terms of efficiency and fault-tolerance. Wang and Anandkumar [23] recently proposed an algorithm for differentially private tensor decomposition using a noisy version of the tensor power iteration [3,8].…”

Section: Introductionmentioning

confidence: 89%

Distributed Differentially Private Algorithms for Matrix and Tensor Factorization

Imtiaz

Sarwate

2018

IEEE J. Sel. Top. Signal Process.

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In many signal processing and machine learning applications, datasets containing private information are held at different locations, requiring the development of distributed privacy-preserving algorithms. Tensor and matrix factorizations are key components of many processing pipelines. In the distributed setting, differentially private algorithms suffer because they introduce noise to guarantee privacy. This paper designs new and improved distributed and differentially private algorithms for two popular matrix and tensor factorization methods: principal component analysis (PCA) and orthogonal tensor decomposition (OTD). The new algorithms employ a correlated noise design scheme to alleviate the effects of noise and can achieve the same noise level as the centralized scenario. Experiments on synthetic and real data illustrate the regimes in which the correlated noise allows performance matching with the centralized setting, outperforming previous methods and demonstrating that meaningful utility is possible while guaranteeing differential privacy.

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Section: Introductionmentioning

confidence: 89%

Distributed Differentially Private Algorithms for Matrix and Tensor Factorization

Imtiaz

Sarwate

2018

IEEE J. Sel. Top. Signal Process.

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“…Experimentally, we compare our proposed algorithm with the existing stateof-the-art algorithm and a non-private algorithm. We show that the proposed algorithm outperforms the conventional privacy-preserving algorithm [1] and Figure 1: The structure of the network: left -conventional, right -CAPE can provide utility very close to that of the non-private algorithm [11] for some parameter choices. We analyze the variation of utility with different privacy levels, number of samples and some other key parameters.…”

Section: Introductionmentioning

confidence: 90%

“…In this paper, we propose a new algorithm, capeDJICA, for ( , δ)-DP decentralized joint ICA. The algorithm significantly improves upon our earlier work [1] by taking advantage of a recently proposed correlation assisted private estimation (CAPE) protocol [19]. Our method adds correlated noise to the output of each site to guarantee privacy locally and a central aggregator combines these noisy outputs to produce an improved estimate.…”

Section: Introductionmentioning

confidence: 92%

Improved Differentially Private Decentralized Source Separation for fMRI Data

Imtiaz,

Mohammadi,

Silva

et al. 2019

Preprint

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Blind source separation algorithms such as independent component analysis (ICA) are widely used in the analysis of neuroimaging data. In order to leverage larger sample sizes, different data holders/sites may wish to collaboratively learn feature representations. However, such datasets are often privacy-sensitive, precluding centralized analyses that pool the data at a single site. A recently proposed algorithm uses message-passing between sites and a central aggregator to perform a decentralized joint ICA (djICA) without sharing the data. However, this method does not satisfy formal privacy guarantees. We propose a differentially private algorithm for performing ICA in a decentralized data setting. Differential privacy provides a formal and mathematically rigorous privacy guarantee by introducing noise into the messages. Conventional approaches to decentralized differentially private algorithms may require too much noise due to the typically small sample sizes at each site. We leverage a recently proposed correlated noise protocol to remedy the excessive noise problem of the conventional schemes. We investigate the performance of the proposed algorithm on synthetic and real fMRI datasets to show that our algorithm outperforms existing approaches and can sometimes reach the same level of utility as the corresponding non-private algorithm. This indicates that it is possible to have meaningful utility while preserving privacy.

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“…Additional extensions to the analysis provided here include reducing the bandwidth of the method and designing privacy-preserving variants that guarantee differential privacy, which we have previously investigated for simulated cases [51]. In such cases, reducing the iteration complexity will help guarantee more privacy and hence incentivize larger research collaborations.…”

Section: Conclusion and Futureworkmentioning

confidence: 99%

Decentralized temporal independent component analysis: Leveraging fMRI data in collaborative settings

et al. 2019

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The field of neuroimaging has recently witnessed a strong shift towards data sharing; however, current collaborative research projects may be unable to leverage contemporary institutional architectures that collect and store data in local, centralized data centers. Although research groups are willing to grant access for collaborations, they often wish to maintain control of their data locally. This may stem from research culture as well as privacy and accountability concerns. In order to leverage the potential of these aggregated larger data sets, we require tools that perform joint analyses without transmitting the data. Ideally, these tools would have similar performance and ease of use as their current centralized counterparts. In this paper, we propose and evaluate a new algorithm, decentralized joint independent component analysis (djICA), which meets these technical requirements. djICA shares only intermediate statistics about the data, retaining privacy of the raw information to local sites and, thus, making it amenable to further privacy protections, for example via differential privacy. We validate our method on real functional magnetic resonance imaging (fMRI) data and show that it enables collaborative large-scale temporal ICA of fMRI, a rich vein of analysis as of yet largely unexplored, and which can benefit from the larger-N studies enabled by a decentralized approach. We show that djICA is robust to different distributions of data over sites, and that the temporal components estimated with djICA show activations similar to the temporal functional modes analyzed in previous work, thus solidifying djICA as a new, decentralized method oriented toward the frontiers of temporal independent component analysis.

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Privacy-preserving source separation for distributed data using independent component analysis

Cited by 8 publications

References 17 publications

Distributed Differentially Private Algorithms for Matrix and Tensor Factorization

Distributed Differentially Private Algorithms for Matrix and Tensor Factorization

Improved Differentially Private Decentralized Source Separation for fMRI Data

Decentralized temporal independent component analysis: Leveraging fMRI data in collaborative settings

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