Lightweight encryption for privacy protection of data transmission in cyber physical systems

Tiwari, Devisha; Mondal, Bhaskar; Singh, Sunil Kumar; Koundal, Deepika

doi:10.1007/s10586-022-03790-1

Cited by 17 publications

(4 citation statements)

References 22 publications

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“…This algorithm can use information permutation to generate pseudo-random sequences and generate new key streams during data transmission, thereby providing protection. The results indicated that the algorithm can effectively resist any existing data attacks [9] . To solve the privacy security of mobile crowd-sourcing data, Wang W and other scholars proposed a mobile crowdsourcing federated learning system on the grounds of blockchain and edge computing.…”

Section: Related Workmentioning

confidence: 94%

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Privacy Protection Based on Federated Learning

Liu,

Eric B. Blancaflor,

Fang

et al. 2024

JAIT

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With the development of artificial intelligence technology, more and more fields will collect relevant user data, and provide users with a better experience through data analysis. But there are also risks involved in the process of data collection, namely how to protect personal privacy data. To address this issue, this study combined differential privacy, convolutional neural networks, and federated averaging algorithms to construct a privacy protection model. The study first utilized the federated average algorithm to handle data imbalance, ensuring that each analyzed data is in a balanced state. Then, based on of data balancing, a new algorithm model was constructed using differential privacy and convolutional neural networks. Finally, it utilized a number of public datasets to verify the role of the model in privacy protection. The results showed that the model can achieve recognition accuracy of 97.27% and 93.15%, respectively , for data under the influence of privacy budget and relaxation factor. Meanwhile, the classification accuracy of the model for data can reached 95.31%, with a regression error of 9.03%. When the local iteration number of the device was 30, the testing accuracy can reached 95.28%. This indicates that methods on the grounds of federated averaging algorithm and differential privacy can maintain the accuracy of the model while protecting user privacy. The application research of models has strong practical significance.

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Section: Related Workmentioning

confidence: 94%

“…In formula(9),  represents the gradient operator. ( ) t f  represents the loss function utilized in the optimization process.…”

mentioning

confidence: 99%

Privacy Protection Based on Federated Learning

Liu,

Eric B. Blancaflor,

Fang

et al. 2024

JAIT

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“…Ensuring that these images remain confidential during transmission, safeguarding against theft or leakage has become a prominent research focus. 4 , 5 , 6 However, due to the distinctiveness of digital images, 7 , 8 , 9 such as high correlation of adjacent pixel points, scattering distribution of critical information, and high information redundancy, cryptographic protection using traditional text encryption algorithms struggles to meet real-time performance requirements. Furthermore, chaos itself exhibits inherent qualities 10 , 11 , 12 , 13 such as high sensitivity to initial conditions and control parameters, excellent pseudo-randomness, ergodicity, and long-term unpredictability of orbits, and it has many similarities with confusion, diffusion, and so on in cryptography.…”

Section: Introductionmentioning

confidence: 99%

Secure image encryption algorithm using chaos-based block permutation and weighted bit planes chain diffusion

Wen,

Lin,

Kang

et al. 2024

iScience

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“…The advent of lightweight ciphers (a new branch of cryptography) enables data to be effectively protected in applications with limited resources 1 . Tiwari et al 2 propose lightweight encryption for privacy protection of data transmission in cyber physical systems. There are several different approaches to developing lightweight ciphers.…”

Section: Introductionmentioning

confidence: 99%

Low area and high throughput hardware implementations for the LILLIPUT cipher

Yang,

Li,

Huang

2023

Circuit Theory & Apps

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SummaryThe widespread use of Internet of Things devices has increased the demand for lower cost and more efficient lightweight ciphers. However, there is a difficult trade‐off between cost and efficiency for lightweight block ciphers. The optimizations of area and throughput are important for some constrained environments. This paper proposes two novel hardware architectures for the LILLIPUT cipher. In the novel low area structure, a new permutation layer is provided for LILLIPUT. The relationship between encryption algorithm and key scheduling algorithm is utilized to achieve optimal sharing among components, which significantly reduces hardware area. The experimental results show that the number of XOR gates and S‐boxes required for low area optimization is reduced by 52 and 8, respectively. The total area is reduced by about 18%. For high throughput structure, this paper provides 2‐round, 5‐round, and 15‐round loop unrolling designs for LILLIPUT to improve throughput. The experimental results show that the throughput of the 5‐round loop unrolling structure reaches a good level, which is relatively the most cost‐effective. In practical application, ciphers can be unrolled implementations according to the needs of devices to improve the execution speed, which can greatly reduce the execution time and complexity of the algorithm.

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Lightweight encryption for privacy protection of data transmission in cyber physical systems

Cited by 17 publications

References 22 publications

Privacy Protection Based on Federated Learning

Privacy Protection Based on Federated Learning

Secure image encryption algorithm using chaos-based block permutation and weighted bit planes chain diffusion

Low area and high throughput hardware implementations for the LILLIPUT cipher

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