Improved Algorithms for Differentially Private Orthogonal Tensor Decomposition

@article{Imtiaz2018ImprovedAF,
  title={Improved Algorithms for Differentially Private Orthogonal Tensor Decomposition},
  author={Hafiz Imtiaz and Anand D. Sarwate},
  journal={2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year={2018},
  pages={2201-2205}
}
Tensor decompositions have applications in many areas including signal processing, machine learning, computer vision and neuroscience. In this paper, we propose two new differentially private algorithms for orthogonal decomposition of symmetric tensors from private or sensitive data; these arise in applications such as latent variable models. Differential privacy is a formal privacy framework that guarantees protections against adversarial inference. We investigate the performance of these… CONTINUE READING

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