Privacy-Preserving Deep Learning on Machine Learning as a Service—a Comprehensive Survey

Tanuwidjaja, Harry Chandra; Choi, Rakyong; Baek, Seunggeun; Kim, Kwangjo

doi:10.1109/access.2020.3023084

Cited by 78 publications

(53 citation statements)

References 91 publications

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“…Therefore, the basic architecture of CNN consists of three layers, namely convolutional (CONV) layers, pooling (POOL) layers and fully connected (FC) layers, as described below [12,14]. Any middle layers are considered hidden since the activation function and final convolution cover their inputs and outputs [15]. Figure 1 illustrates the basic architecture of a CNN model.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

An Efficient Approach Based on Privacy-Preserving Deep Learning for Satellite Image Classification

et al. 2021

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Satellite images have drawn increasing interest from a wide variety of users, including business and government, ever since their increased usage in important fields ranging from weather, forestry and agriculture to surface changes and biodiversity monitoring. Recent updates in the field have also introduced various deep learning (DL) architectures to satellite imagery as a means of extracting useful information. However, this new approach comes with its own issues, including the fact that many users utilize ready-made cloud services (both public and private) in order to take advantage of built-in DL algorithms and thus avoid the complexity of developing their own DL architectures. However, this presents new challenges to protecting data against unauthorized access, mining and usage of sensitive information extracted from that data. Therefore, new privacy concerns regarding sensitive data in satellite images have arisen. This research proposes an efficient approach that takes advantage of privacy-preserving deep learning (PPDL)-based techniques to address privacy concerns regarding data from satellite images when applying public DL models. In this paper, we proposed a partially homomorphic encryption scheme (a Paillier scheme), which enables processing of confidential information without exposure of the underlying data. Our method achieves robust results when applied to a custom convolutional neural network (CNN) as well as to existing transfer learning methods. The proposed encryption scheme also allows for training CNN models on encrypted data directly, which requires lower computational overhead. Our experiments have been performed on a real-world dataset covering several regions across Saudi Arabia. The results demonstrate that our CNN-based models were able to retain data utility while maintaining data privacy. Security parameters such as correlation coefficient (−0.004), entropy (7.95), energy (0.01), contrast (10.57), number of pixel change rate (4.86), unified average change intensity (33.66), and more are in favor of our proposed encryption scheme. To the best of our knowledge, this research is also one of the first studies that applies PPDL-based techniques to satellite image data in any capacity.

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Section: Convolutional Neural Networkmentioning

confidence: 99%

An Efficient Approach Based on Privacy-Preserving Deep Learning for Satellite Image Classification

et al. 2021

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“…Still, they only considered the security of the DL‐based model. References 7‐11 presented ML‐ and DL‐based surveys and elaborated the adversarial attacks and their techniques to generate attack and how that specific attack is mitigated by different defense mechanism and discussed other security‐based mechanisms such as differential privacy, HE, security enclaves, and secure multiparty communication. These surveys addressed the security and privacy issues of the ML and DL model and gave the research directions to protect against adversarial attacks.…”

Section: Introductionmentioning

confidence: 99%

Adversarial learning techniques for security and privacy preservation: A comprehensive review

Hathaliya

Tanwar

Sharma

2022

Security and Privacy

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In recent years, the use of smart devices has increased exponentially, resulting in massive amounts of data. To handle this data, effective data storage and management has required. Cloud computing (CC) is a promising solution to deal with this huge amount of data. Electronic devices are collecting real‐time data from sensors and applications through a wireless communication channel in the digital era. In some cases, CC cannot protect against various malicious attacks in the wireless communication channel. To address this issue, we have used machine learning (ML) and deep learning (DL) techniques for attack detection in a wireless channel on an early basis. It trains a model to predict malicious activities of attackers, which aids in the security of CC's sensitive data. We employed adversarial learning techniques (AL) to add fake data into the model to ensure that the trained model was correct. The trained model can distinguish between the fake and real data from the training samples and improve the training samples' performance. AL provides different defense mechanisms to preserve the privacy of ML‐ and DL‐based model but does not ensure the system's robustness. To improve the system's robustness, we have used federated learning with blockchain technology to make a system more robust, reliable, accurate, and transparent. This integration aids in providing high‐graded security against adversarial attacks. This paper presents a comprehensive review to highlight the recent improvements in AL techniques. Moreover, we explored the various AL applications in security and privacy preservation. Finally, open research issues and future directions are discussed to show future research avenues.

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“…Protocols used for private machine learning training are investigated in [29]. Similarily, Tanuwidjaja et al [30] summarize existing works on privacy-preserving deep learning and issues when using these schemes as well as possible attacks on private deep learning. Kiss et al [31] systematically review the state-of-the-art approaches to private decision tree evaluation.…”

Section: Introductionmentioning

confidence: 99%

SoK: Efficient Privacy-preserving Clustering

Hegde

Möllering

Schneider

et al. 2021

Proceedings on Privacy Enhancing Technologies

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Clustering is a popular unsupervised machine learning technique that groups similar input elements into clusters. It is used in many areas ranging from business analysis to health care. In many of these applications, sensitive information is clustered that should not be leaked. Moreover, nowadays it is often required to combine data from multiple sources to increase the quality of the analysis as well as to outsource complex computation to powerful cloud servers. This calls for efficient privacy-preserving clustering. In this work, we systematically analyze the state-of-the-art in privacy-preserving clustering. We implement and benchmark today’s four most efficient fully private clustering protocols by Cheon et al. (SAC’19), Meng et al. (ArXiv’19), Mohassel et al. (PETS’20), and Bozdemir et al. (ASIACCS’21) with respect to communication, computation, and clustering quality. We compare them, assess their limitations for a practical use in real-world applications, and conclude with open challenges.

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Privacy-Preserving Deep Learning on Machine Learning as a Service—a Comprehensive Survey

Cited by 78 publications

References 91 publications

An Efficient Approach Based on Privacy-Preserving Deep Learning for Satellite Image Classification

An Efficient Approach Based on Privacy-Preserving Deep Learning for Satellite Image Classification

Adversarial learning techniques for security and privacy preservation: A comprehensive review

SoK: Efficient Privacy-preserving Clustering

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