ArticleIEEE journal of selected topics in signal processing2018
Distributed Differentially-Private Algorithms for Matrix and Tensor Factorization.
Article in IEEE journal of selected topics in signal processing, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- CEDAR: communication efficient distributed analysis for regressions.Biometrics · 2023Article
- Approximating Functions with Approximate Privacy for Applications in Signal Estimation and Learning.Entropy (Basel, Switzerland) · 2023Article
- NeuroCrypt: Machine Learning Over Encrypted Distributed Neuroimaging Data.Neuroinformatics · 2022Article
- A Correlated Noise-assisted Decentralized Differentially Private Estimation Protocol, and its application to fMRI Source Separation.IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society · 2021Article
- Binary matrix factorization on special purpose hardware.PloS one · 2021Article
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Authors and funding
2 authors.
Funding
Abstract
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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