49,237 research outputs found
NegDL: Privacy-Preserving Deep Learning Based on Negative Database
In the era of big data, deep learning has become an increasingly popular
topic. It has outstanding achievements in the fields of image recognition,
object detection, and natural language processing et al. The first priority of
deep learning is exploiting valuable information from a large amount of data,
which will inevitably induce privacy issues that are worthy of attention.
Presently, several privacy-preserving deep learning methods have been proposed,
but most of them suffer from a non-negligible degradation of either efficiency
or accuracy. Negative database (\textit{NDB}) is a new type of data
representation which can protect data privacy by storing and utilizing the
complementary form of original data. In this paper, we propose a
privacy-preserving deep learning method named NegDL based on \textit{NDB}.
Specifically, private data are first converted to \textit{NDB} as the input of
deep learning models by a generation algorithm called \textit{QK}-hidden
algorithm, and then the sketches of \textit{NDB} are extracted for training and
inference. We demonstrate that the computational complexity of NegDL is the
same as the original deep learning model without privacy protection.
Experimental results on Breast Cancer, MNIST, and CIFAR-10 benchmark datasets
demonstrate that the accuracy of NegDL could be comparable to the original deep
learning model in most cases, and it performs better than the method based on
differential privacy
FedPNN: One-shot Federated Classification via Evolving Clustering Method and Probabilistic Neural Network hybrid
Protecting data privacy is paramount in the fields such as finance, banking,
and healthcare. Federated Learning (FL) has attracted widespread attention due
to its decentralized, distributed training and the ability to protect the
privacy while obtaining a global shared model. However, FL presents challenges
such as communication overhead, and limited resource capability. This motivated
us to propose a two-stage federated learning approach toward the objective of
privacy protection, which is a first-of-its-kind study as follows: (i) During
the first stage, the synthetic dataset is generated by employing two different
distributions as noise to the vanilla conditional tabular generative
adversarial neural network (CTGAN) resulting in modified CTGAN, and (ii) In the
second stage, the Federated Probabilistic Neural Network (FedPNN) is developed
and employed for building globally shared classification model. We also
employed synthetic dataset metrics to check the quality of the generated
synthetic dataset. Further, we proposed a meta-clustering algorithm whereby the
cluster centers obtained from the clients are clustered at the server for
training the global model. Despite PNN being a one-pass learning classifier,
its complexity depends on the training data size. Therefore, we employed a
modified evolving clustering method (ECM), another one-pass algorithm to
cluster the training data thereby increasing the speed further. Moreover, we
conducted sensitivity analysis by varying Dthr, a hyperparameter of ECM at the
server and client, one at a time. The effectiveness of our approach is
validated on four finance and medical datasets.Comment: 27 pages, 13 figures, 7 table
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