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.

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