Journal
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
Volume 17, Issue -, Pages 152-164Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIFS.2021.3089884
Keywords
Differential privacy; deep learning; stochastic gradient descent
Funding
- NSF, China [61902245, 62032020, 61960206002, 61822206, 62020106005, 61829201]
- Science and Technology Innovation Program of Shanghai [19YF1424500]
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This study introduces a new (ε, δ)-differential privacy mechanism TVG for protecting privacy in tensor-valued queries, with improved utility by applying unimodal differentially-private noise. Experimental results demonstrate that TVG outperforms other state-of-the-art mechanisms in tensor-valued queries.
Private individual information are increasingly exposed through high-dimensional and high-order data, with the wide deployment of learning techniques. These data are typically expressed in form of tensors, but there is no principled way to guarantee privacy for tensor-valued queries. Conventional differential privacy is typically applied to scalar values without a precise definition on the shape of the queried data. Realizing that the conventional mechanisms do not take the data structural information into account, we propose Tensor Variate Gaussian (TVG), a new (epsilon, delta)-differential privacy mechanism for tensor-valued queries. We further introduce two mechanisms based on TVG with an improved utility by imposing the unimodal differentially-private noise. With the utility space available, the proposed mechanisms can be instantiated with an optimized utility, and the optimization problem has a closed-form solution scalable to large-scale problems. Finally, we experimentally test our mechanisms on a variety of datasets and models, demonstrating that TVG is superior than other state-of-the-art mechanisms on tensor-valued queries.
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