Distributed Image Encryption Based On a Homomorphic Cryptographic Approach

Wade, Mamadou; Chouikha, Mohamed F.; Gill, Tepper L.; Patterson, Wayne; Washington, Talitha M.; Zeng, Jianchao

doi:10.1109/uemcon47517.2019.8993025

Cited by 13 publications

(4 citation statements)

References 17 publications

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“…According to the results of their experiment, the authors confirmed that this technique can significantly reduce the load on IoT devices. The work presented in [7] introduces a new distributed homomorphic image encryption approach. In the encryption phase, the original RGB image is divided into its R, G, and B-channel components.…”

Section: Related Workmentioning

confidence: 99%

A New Scalable Distributed Homomorphic Encryption Scheme for High Computational Complexity Models

Almaini,

Folz,

Woelfl

et al. 2023

2023 International Wireless Communications and Mobile Computing (IWCMC)

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Due to the increasing privacy demand in data processing, Fully Homomorphic Encryption (FHE) has recently received growing attention for its ability to perform calculations over encrypted data. Since the data can be processed in encrypted form and the output remains encrypted, only an authorized user or a user who holds the key can decrypt the data and understand its meaning. Hence, it is possible to securely outsource data processing to untrustworthy but powerful public computing resources on the edge. However, due to the high computational complexity, FHE-based data processing experiences scalability related concerns. It is currently unclear whether FHE can be used to solve large-scale problems. In this paper, we propose a novel general distributed FHE-based data processing approach as a concrete step towards solving the scalability challenge. The main idea behind our approach is to use slightly more communication overhead for a shorter computing circuit in FHE, hence, reducing the overall complexity. We verify our new model's efficiency and effectiveness by comparing the distributed approach with the central approach over various FHE schemes (CKKS, BGV, and BFV). This is performed using one of the more popular libraries of FHE "Microsoft SEAL", by performing specific mathematical operations and observing the time consumed. The empirical results demonstrate that the proposed approach results in a significant reduction in time, up to 54% compared to the traditional central approach.

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Section: Related Workmentioning

confidence: 99%

A New Scalable Distributed Homomorphic Encryption Scheme for High Computational Complexity Models

Almaini,

Folz,

Woelfl

et al. 2023

2023 International Wireless Communications and Mobile Computing (IWCMC)

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“…In Goel, Gupta & Kaushik (2014-2015) , the researches focuses on a new way of embedding hidden messages into cover images utilizing the LSB method with various progressions and a homomorphic cryptographic approach. In Wade et al (2019) , mean square error (MSE), peak signal to noise ratio (PSNR), structure similiarity index (SSIM), CPU time, histograms and feature similarity index measurement (FSIM) was used to compare the stego picture to the cover image. Their research and experiments reveal that their proposed approach is faster and more efficient than traditional LSB methods.…”

Section: Related Workmentioning

confidence: 99%

An efficient and secure technique for image steganography using a hash function

Nezami

Ali

Asif

et al. 2022

PeerJ Computer Science

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Steganography is a technique in which a person hides information in digital media. The message sent by this technique is so secret that other people cannot even imagine the information’s existence. This article entails developing a mechanism for communicating one-on-one with individuals by concealing information from the rest of the group. Based on their availability, digital images are the most suited components for use as transmitters when compared to other objects available on the internet. The proposed technique encrypts a message within an image. There are several steganographic techniques for hiding hidden information in photographs, some of which are more difficult than others, and each has its strengths and weaknesses. The encryption mechanism employed may have different requirements depending on the application. For example, certain applications may require complete invisibility of the key information, while others may require the concealment of a larger secret message. In this research, we proposed a technique that converts plain text to ciphertext and encodes it in a picture using up to the four least significant bit (LSB) based on a hash function. The LSBs of the image pixel values are used to substitute pieces of text. Human eyes cannot predict the variation between the initial Image and the resulting image since only the LSBs are modified. The proposed technique is compared with state-of-the-art techniques. The results reveal that the proposed technique outperforms the existing techniques concerning security and efficiency with adequate MSE and PSNR.

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“…Therefore, many researchers have proposed various solutions, such as federated learning and homomorphic encryption based on secure multi-party computation. However, both of the above methods have the disadvantage of high computing and communication costs [14]- [21], while the computing and communication budget of federated learning based on differential privacy technology is much smaller [22].In the field of differential privacy, TRIASTCYN et al proposed Bayesian differential privacy, which improves the model accuracy by 10% and achieves the privacy assurance effect with less noise [23]; SUN et al proposed the local differential privacy aggregation mechanism, which can improve the model training performance and less communication rounds [24]; NASERI et al proposed the local and central differential privacy mechanism, which has the advantages of strong defensive aggression and effective reduction of attack accuracy [25]; KIM et al's pioneering combination of FedSGD with differential privacy mechanism requires results of small noise variance, high utility, low transmission rate [26]; XIN et al proposed a generative adversarial network model based on differential privacy, which can generate high-quality synthetic data and have high model performance [27]Nowadays, federated learning based on differential privacy technology mainly has two aspects: 1) Using centralized differential privacy to disturb the central server [28];2) Local differential privacy (LDP) perturbation uploaded model parameters [29]. None of the above differential privacy methods consider the personalized needs of users.…”

Section: Introductionmentioning

confidence: 99%

Personalized differential privacy approach in the federated learning scenario

Guo,

Dong,

Liu

2024

Second International Conference on Informatics, Networking, and Computing (ICINC 2023)

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In order to deal with the problem of model gradients, neural network weights and other parameters exposing user privacy information and user privacy personalization under federated learning, therefore, this article proposes a personalized differential privacy method in a federated learning scenario. Before training, users upload the privacy protection level to the central server. The central server defines the amount of noise based on the total privacy budget and privacy protection level, and aggregates the training parameters using an aggregation algorithm weighted by the privacy protection level; the algorithm is verified to be effective in MNIST and CIFAR-10 data set and compared with existing localized differential privacy algorithms and centralized differential privacy algorithms, The classification accuracy is 94.12% and 45.20% respectively. Proved the usability and rationality of the algorithm in this scenario.

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Distributed Image Encryption Based On a Homomorphic Cryptographic Approach

Cited by 13 publications

References 17 publications

A New Scalable Distributed Homomorphic Encryption Scheme for High Computational Complexity Models

A New Scalable Distributed Homomorphic Encryption Scheme for High Computational Complexity Models

An efficient and secure technique for image steganography using a hash function

Personalized differential privacy approach in the federated learning scenario

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