Cloud-RSA: An Enhanced Homomorphic Encryption Scheme

Makkaoui, Khalid El; Ezzati, Abdellah; Beni‐Hssane, Abderrahim

doi:10.1007/978-3-319-46568-5_48

Cited by 12 publications

(9 citation statements)

References 9 publications

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“…It also defined the properties of multiplicative HE. However, strong security principles are not necessarily fulfilled here because, in order to accomplish semantic security, RSA must pad a message with random bits before encryption, which results in losing the homomorphic property [48]. Since RSA does not fulfill strong security requirements, then, this research implementation is based on the Paillier scheme.…”

Section: Partially Homomorphic Encryption Schemesmentioning

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: Partially Homomorphic Encryption Schemesmentioning

confidence: 99%

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

et al. 2021

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“…Makkaoui [74] proposed an improved RSA encryption method in 2017, which maintains the homomorphic feature and is more resistant to well-known RSA attacks. Although it maintains a homomorphic feature, it is best suited for preserving the secrecy of data that has been outsourced to a third party.…”

Section: Cloudmentioning

confidence: 99%

“…Aside from boosting speed, using the improved method makes the computation more complicated and enhances the security strength. To speed up the Cloud-RSA [74] decryption, Makkaoui [76] proposed a fast variation of the Cloud-RSA encryption method in 2017. The fast Cloud-RSA employs a modulus of the type N = p r q s for r, s ≥ 2.…”

Section: Cloudmentioning

confidence: 99%

Systematic and Critical Review of RSA Based Public Key Cryptographic Schemes: Past and Present Status

et al. 2021

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“…1. In order to train ML models upon ciphertext without accuracy loss, we design a library of building blocks, based on the additively homomorphic encryption Paillier [37] and multiplicative homomorphic encryption Cloud-RSA [38]. Inspired by the idea of permutations and combinations that complex algorithms can be decomposed into several primitive operations, after identifying a set of core operations that underlie many ML training algorithms, we carefully design a library of building blocks supporting each of these core operations.…”

Section: Computation Modementioning

confidence: 99%

“…Some cryptosystems are gifted with a homomorphic property that can perform a set of operations on encrypted data without knowledge of the decryption key. Formalized definition of homomorphic can be found in Appendix L. a message encrypted by a non-specified cryptosystems Two public-key cryptosystems are employed in this paper: Paillier [37] and Cloud-RSA [38]. Paillier possesses additively homomorphic properties, and Cloud-RSA possesses multiplicative.…”

Section: Preliminarymentioning

confidence: 99%

Privacy-Preserving Machine Learning Training in Aggregation Scenarios

Zhu,

Tang,

Shen

et al. 2020

Preprint

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To develop Smart City, the growing popularity of Machine Learning (ML) that appreciates high-quality training datasets generated from diverse IoT devices raises natural questions about the privacy guarantees that can be provided in such settings. Privacy-preserving ML training in an aggregation scenario enables a model demander to securely train ML models with the sensitive IoT data gathered from personal IoT devices. Existing solutions are generally server-aided, cannot deal with the collusion threat between the servers or between the servers and data owners, and do not match the delicate environments of IoT. We propose a privacy-preserving ML training framework named Heda that consists of a library of building blocks based on partial homomorphic encryption (PHE) enabling constructing multiple privacy-preserving ML training protocols for the aggregation scenario without the assistance of untrusted servers and defending the security under collusion situations. Rigorous security analysis demonstrates the proposed protocols can protect the privacy of each participant in the honest-but-curious model and defend the security under most collusion situations. Extensive experiments validate the efficiency of Heda which achieves the privacypreserving ML training without losing the model accuracy.

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Cloud-RSA: An Enhanced Homomorphic Encryption Scheme

Cited by 12 publications

References 9 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

Systematic and Critical Review of RSA Based Public Key Cryptographic Schemes: Past and Present Status

Privacy-Preserving Machine Learning Training in Aggregation Scenarios

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