Preserving differential privacy in deep neural networks with relevance-based adaptive noise imposition

Gong, Maoguo; Pan, Ke; Xie, Yu; Qin, A. K.; Tang, Zedong

doi:10.1016/j.neunet.2020.02.001

Cited by 47 publications

(25 citation statements)

References 44 publications

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“…One of the most common privacy attacks is an inference attack where the adversary maliciously infers sensitive features and background information about a target individual from a trained model [60]. Typically, there are two types of inference attacks in deep learning.…”

Section: Privacy Attacks In the Deep Neural Networkmentioning

confidence: 99%

More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence

Zhu¹,

Ye²,

Wang³

et al. 2022

IEEE Trans. Knowl. Data Eng.

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Artificial Intelligence (AI) has attracted a great deal of attention in recent years. However, alongside all its advancements, problems have also emerged, such as privacy violations, security issues and model fairness. Differential privacy, as a promising mathematical model, has several attractive properties that can help solve these problems, making it quite a valuable tool. For this reason, differential privacy has been broadly applied in AI but to date, no study has documented which differential privacy mechanisms can or have been leveraged to overcome its issues or the properties that make this possible. In this paper, we show that differential privacy can do more than just privacy preservation. It can also be used to improve security, stabilize learning, build fair models, and impose composition in selected areas of AI. With a focus on regular machine learning, distributed machine learning, deep learning, and multi-agent systems, the purpose of this article is to deliver a new view on many possibilities for improving AI performance with differential privacy techniques.

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Section: Privacy Attacks In the Deep Neural Networkmentioning

confidence: 99%

More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence

Zhu¹,

Ye²,

Wang³

et al. 2022

IEEE Trans. Knowl. Data Eng.

View full text Add to dashboard Cite

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“…Recently, there have been some further works on differentially private deep learning [14,16,20,21,29,30,37,41,43,44]. Most of them are the variants of the private SGD algorithm that is based on the gradient perturbation.…”

Section: Related Workmentioning

confidence: 99%

“…Following the work of Reference [41], Xu et al [43] proposed an adaptive and fast convergent differentially private algorithm that adjusted the learning rate in an adaptive manner and added the sensitivity-dependent noise to obtain the more proper privacy protection. Some other work such as References [14,30] paid attention to the relevance of different features to the model output and presented the adaptive noise addition mechanism based on the relevance analysis. Besides, some researchers undertook their work on privacy loss accounting.…”

Section: Related Workmentioning

confidence: 99%

“…There are few studies considering the other gradient descent optimization algorithms. To our best knowledge, only References [14,19] mentioned the differentially private gradient descent optimization algorithm but they only simply presented the private algorithm that just conducted the single one optimization for the perturbed gradient in each training step and the privacy accounting analysis was not considered in their work. Compared with their works, we integrate the gradient descent optimization algorithm as an iterative component into the differentially private deep learning framework, and it is beneficial for increasing the training speeding and adding the noise adaptively.…”

Section: :5mentioning

confidence: 99%

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Differentially Private Deep Learning with Iterative Gradient Descent Optimization

Ding

Chen

Zhou

et al. 2021

ACM/IMS Trans. Data Sci.

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Deep learning has achieved great success in various areas and its success is closely linked to the availability of massive data. But in general, a large dataset could include sensitive data and therefore the model should have the capability to avoid privacy leakage. To achieve this aim, many works apply the famous privacy framework named differential privacy into deep learning to preserve privacy. In this article, we propose a novel perturbed iterative gradient descent optimization (PIGDO) algorithm and prove that this algorithm satisfies the differential privacy. Besides, we propose a modified moments accountant (MMA) method to conduct the privacy analysis and obtain a tighter bound of privacy loss compared with the original moments accountant method. A number of experiments demonstrate that our optimization algorithm can not only improve the model accuracy and training speed, but also achieve better privacy guarantees over the state-of-the-art algorithm while reaching the equivalent accuracy. We provide codes for all of our experiments in https://github.com/CGCL-codes/DPDLIGDO.git .

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“…The second facet of the research is to assess the sensitivity level of the information that is disseminated from the database based on the analysts" queries. The issue of utility-based privacy controlling data mining was reviewed in [11]. In [12], a technique for the suppression of anonymization of data.…”

Section: Related Workmentioning

confidence: 99%

Improving the Performance of Various Privacy Preserving Databases using Hybrid Geometric Data Perturbation Classification Model

Gouse¹,

Mohan²

2020

IJACSA

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As the size of the privacy preserving databases is increasing, it is difficult to improve the privacy and accuracy of these databases due to dimensionality and runtime. However, most of the traditional privacy preserving models are independent of privacy and runtime. Also, it is essential to preserve the privacy of the large sensitive attributes before publishing it to the third-party servers. As a result, a novel framework is required to improve the privacy as well as accuracy on the high dimensional privacy preserving data with less runtime. In order to improve the privacy, accuracy and runtime of the traditional privacy preserving models, a hybrid perturbation based privacy preserving classification model is proposed on the multiple databases. In this work, a new data transformation approach, hybrid geometrical perturbation approach and hybrid boosting classifier are proposed in order to enhance the overall efficiency of the model on the privacy preserving databases. In this work, a hybrid geometric perturbation approach is used to enhance the privacy preserving on the sensitive attributes. Initially, a pre-processing method is applied on the input dataset in order to remove the noise in the feature values. A hybrid machine learning classifier is proposed to predict the privacy preserving class label based on the training data. Experimental results represents the proposed hybrid geometric perturbation based boosting classifier has better statistical accuracy, recall, precision and runtime than the conventional models.

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Preserving differential privacy in deep neural networks with relevance-based adaptive noise imposition

Cited by 47 publications

References 44 publications

More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence

More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence

Differentially Private Deep Learning with Iterative Gradient Descent Optimization

Improving the Performance of Various Privacy Preserving Databases using Hybrid Geometric Data Perturbation Classification Model

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