# Protecting Big Data Privacy Using Randomized Tensor Network Decomposition and Dispersed Tensor Computation

arXiv: https://arxiv.org/abs/2101.04194 · HTML: https://mlsanity.com/paper/2101.04194/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 78 references that are in the map.
- First posted on arXiv 4 Jan 2021.

## Builds on (most cited first, 18 of 18)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Tensor Decomposition for Signal Processing and Machine Learning](https://mlsanity.com/paper/1607.01668/) (2016)
- [Tensor Ring Decomposition](https://mlsanity.com/paper/1606.05535/) (2016)
- [Synthetic Data for Deep Learning](https://mlsanity.com/paper/1909.11512/) (2019)
- [Tensor Networks for Dimensionality Reduction and Large-Scale Optimizations. Part 2 Applications and Future Perspectives](https://mlsanity.com/paper/1708.09165/) (2017)
- [Tensor Completion Algorithms in Big Data Analytics](https://mlsanity.com/paper/1711.10105/) (2017)
- [TTHRESH: Tensor Compression for Multidimensional Visual Data](https://mlsanity.com/paper/1806.05952/) (2018)
- [Federated Tensor Factorization for Computational Phenotyping](https://mlsanity.com/paper/1704.03141/) (2017)
- [Fast Differentially Private Matrix Factorization](https://mlsanity.com/paper/1505.01419/) (2015)
- [Linked Component Analysis from Matrices to High Order Tensors: Applications to Biomedical Data](https://mlsanity.com/paper/1508.07416/) (2015)
- [Privacy-Preserving Tensor Factorization for Collaborative Health Data Analysis](https://mlsanity.com/paper/1908.09888/) (2019)
- [Online and Differentially-Private Tensor Decomposition](https://mlsanity.com/paper/1606.06237/) (2016)
- [Distributed Differentially-Private Algorithms for Matrix and Tensor Factorization](https://mlsanity.com/paper/1804.10299/) (2018)
- [Human Gait Database for Normal Walk Collected by Smartphone Accelerometer](https://mlsanity.com/paper/1905.03109/) (2019)
- [Stable, Robust and Super Fast Reconstruction of Tensors Using Multi-Way Projections](https://mlsanity.com/paper/1406.3295/) (2014)
- [Data protection by means of fragmentation in various different distributed storage systems - a survey](https://mlsanity.com/paper/1706.05960/) (2017)
- [Privacy-preserving data outsourcing in the cloud via semantic data splitting](https://mlsanity.com/paper/1707.00445/) (2017)

## Built on by (most cited first, 2 of 2)

- [Quantum-inspired Techniques in Tensor Networks for Industrial Contexts](https://mlsanity.com/paper/2404.11277/) (2024)
- [T'ecnicas Quantum-Inspired en Tensor Networks para Contextos Industriales](https://mlsanity.com/paper/2404.17645/) (2024)

## Abstract (arXiv)

Data privacy is an important issue for organizations and enterprises to securely outsource data storage, sharing, and computation on clouds / fogs. However, data encryption is complicated in terms of the key management and distribution; existing secure computation techniques are expensive in terms of computational / communication cost and therefore do not scale to big data computation. Tensor network decomposition and distributed tensor computation have been widely used in signal processing and machine learning for dimensionality reduction and large-scale optimization. However, the potential of distributed tensor networks for big data privacy preservation have not been considered before, this motivates the current study. Our primary intuition is that tensor network representations are mathematically non-unique, unlinkable, and uninterpretable; tensor network representations naturally support a range of multilinear operations for compressed and distributed / dispersed computation. Therefore, we propose randomized algorithms to decompose big data into randomized tensor network representations and analyze the privacy leakage for 1D to 3D data tensors. The randomness mainly comes from the complex structural information commonly found in big data; randomization is based on controlled perturbation applied to the tensor blocks prior to decomposition. The distributed tensor representations are dispersed on multiple clouds / fogs or servers / devices with metadata privacy, this provides both distributed trust and management to seamlessly secure big data storage, communication, sharing, and computation. Experiments show that the proposed randomization techniques are helpful for big data anonymization and efficient for big data storage and computation.