An adaptive federated learning scheme with differential privacy preserving

The innovation of the deep learning modeling scheme plays an important role in promoting the research of complex problems handled with artificial intelligence in smart cities and the development of the next generation of information technology. With the widespread use of smart interactive devices and systems, the exponential growth of data volume and the complex modeling requirements increase the difficulty of deep learning modeling, and the classical centralized deep learning modeling scheme has encountered bottlenecks in the improvement of model performance and the diversification of smart application scenarios. The parallel processing system in deep learning links the virtual information space with the physical world, although the distributed deep learning research has become a crucial concern with its unique advantages in training efficiency, and improving the availability of trained models and preventing privacy disclosure are still the main challenges faced by related research. To address these above issues in distributed deep learning, this research developed a clonal selective optimization system based on the federated learning framework for the model training process involving large-scale data. This system adopts the heuristic clonal selective strategy in local model optimization and optimizes the effect of federated training. First of all, this process enhances the adaptability and robustness of the federated learning scheme and improves the modeling performance and training efficiency. Furthermore, this research attempts to improve the privacy security defense capability of the federated learning scheme for big data through differential privacy preprocessing. The simulation results show that the proposed clonal selection optimization system based on federated learning has significant optimization ability on model basic performance, stability, and privacy.

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Inductive learning and local differential privacy for privacy-preserving offloading in mobile edge intelligent systems

10.36227/techrxiv.13698883 ◽

2021 ◽

Author(s):

Jude TCHAYE-KONDI ◽

Yanlong Zhai ◽

Liehuang Zhu

Keyword(s):

Intelligent Systems ◽

Differential Privacy ◽

Inductive Learning ◽

Random Noise ◽

Privacy Preserving ◽

Sensitive Data ◽

Privacy Concerns ◽

Feature Extractor ◽

Data Source ◽

Series Of Experiments

<div>We address privacy and latency issues in the edge/cloud computing environment while training a centralized AI model. In our particular case, the edge devices are the only data source for the model to train on the central server. Current privacy-preserving and reducing network latency solutions rely on a pre-trained feature extractor deployed on the devices to help extract only important features from the sensitive dataset. However, finding a pre-trained model or pubic dataset to build a feature extractor for certain tasks may turn out to be very challenging. With the large amount of data generated by edge devices, the edge environment does not really lack data, but its improper access may lead to privacy concerns. In this paper, we present DeepGuess , a new privacy-preserving, and latency aware deeplearning framework. DeepGuess uses a new learning mechanism enabled by the AutoEncoder(AE) architecture called Inductive Learning, which makes it possible to train a central neural network using the data produced by end-devices while preserving their privacy. With inductive learning, sensitive data remains on devices and is not explicitly involved in any backpropagation process. The AE’s Encoder is deployed on devices to extracts and transfers important features to the server. To enhance privacy, we propose a new local deferentially private algorithm that allows the Edge devices to apply random noise to features extracted from their sensitive data before transferred to an untrusted server. The experimental evaluation of DeepGuess demonstrates its effectiveness and ability to converge on a series of experiments.</div>

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Privacy-preserving governmental data publishing: A fog-computing-based differential privacy approach

Future Generation Computer Systems ◽

10.1016/j.future.2018.07.038 ◽

2019 ◽

Vol 90 ◽

pp. 158-174 ◽

Cited By ~ 13

Author(s):

Chunhui Piao ◽

Yajuan Shi ◽

Jiaqi Yan ◽

Changyou Zhang ◽

Liping Liu

Keyword(s):

Differential Privacy ◽

Fog Computing ◽

Privacy Preserving ◽

Data Publishing

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Security and Privacy Challenges of Deep Learning

Deep Learning Strategies for Security Enhancement in Wireless Sensor Networks - Advances in Information Security, Privacy, and Ethics ◽

10.4018/978-1-7998-5068-7.ch003 ◽

2020 ◽

pp. 42-64

Author(s):

J. Andrew Onesimu ◽

Karthikeyan J. ◽

D. Samuel Joshua Viswas ◽

Robin D Sebastian

Keyword(s):

Deep Learning ◽

Differential Privacy ◽

Homomorphic Encryption ◽

Detailed Comparison ◽

Privacy Preserving ◽

Research Field ◽

Security And Privacy ◽

Security Attacks ◽

Privacy Breaches ◽

Comparison Table

Deep learning is the buzz word in recent times in the research field due to its various advantages in the fields of healthcare, medicine, automobiles, etc. A huge amount of data is required for deep learning to achieve better accuracy; thus, it is important to protect the data from security and privacy breaches. In this chapter, a comprehensive survey of security and privacy challenges in deep learning is presented. The security attacks such as poisoning attacks, evasion attacks, and black-box attacks are explored with its prevention and defence techniques. A comparative analysis is done on various techniques to prevent the data from such security attacks. Privacy is another major challenge in deep learning. In this chapter, the authors presented an in-depth survey on various privacy-preserving techniques for deep learning such as differential privacy, homomorphic encryption, secret sharing, and secure multi-party computation. A detailed comparison table to compare the various privacy-preserving techniques and approaches is also presented.

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Differentially Private Image Classification Using Support Vector Machine and Differential Privacy

Machine Learning and Knowledge Extraction ◽

10.3390/make1010029 ◽

2019 ◽

Vol 1 (1) ◽

pp. 483-491 ◽

Cited By ~ 6

Author(s):

Makhamisa Senekane

Keyword(s):

Support Vector Machine ◽

Data Analysis ◽

Image Classification ◽

Differential Privacy ◽

Privacy Preserving ◽

Global Optimum ◽

Support Vector ◽

Sensitive Data ◽

Radiological Images ◽

Golden Standard

The ubiquity of data, including multi-media data such as images, enables easy mining and analysis of such data. However, such an analysis might involve the use of sensitive data such as medical records (including radiological images) and financial records. Privacy-preserving machine learning is an approach that is aimed at the analysis of such data in such a way that privacy is not compromised. There are various privacy-preserving data analysis approaches such as k-anonymity, l-diversity, t-closeness and Differential Privacy (DP). Currently, DP is a golden standard of privacy-preserving data analysis due to its robustness against background knowledge attacks. In this paper, we report a scheme for privacy-preserving image classification using Support Vector Machine (SVM) and DP. SVM is chosen as a classification algorithm because unlike variants of artificial neural networks, it converges to a global optimum. SVM kernels used are linear and Radial Basis Function (RBF), while ϵ -differential privacy was the DP framework used. The proposed scheme achieved an accuracy of up to 98%. The results obtained underline the utility of using SVM and DP for privacy-preserving image classification.

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