In this paper, an intelligent dynamic perturbation orthogonal chirp division multiplexing (OCDM) encryption scheme based on cellular neural network and biological genetic encoding for seven-core optical fiber is proposed for the first time to our knowledge. In this scheme, chaotic sequences generated by cellular neural network are employed to construct six masking vectors to achieve six dimensions of ultra-high security encryption. The transmitted bit data is interleaved according to the DNA operation rules. The subcarrier frequency, symbol matrix, and time are scrambled. Because the selected encoding rule, decoding rule, key base sequence, subcarrier frequency, symbol matrix, and scrambling position of time all change dynamically, the robustness against malicious attack is enhanced. Simultaneously, OCDM technology is employed to optimize the system, which effectively improves the anti-interference ability and bit error performance of the system. A 70 Gb /s (7×10 Gb /s) encrypted OCDM signal transmission experiment is carried out on a 2 km 7-core fiber, and an orthogonal frequency division multiplexing (OFDM) signal is transmitted under the same conditions for comparison and verification. The results show that the key space of the newly proposed encryption scheme can reach 10, and the receiver sensitivity of OCDM is 1.2 dB greater than that of OFDM when the bit error rate is 10. The scheme can improve the security of encrypted information and the performance of the system, which is very promising in the optical access network of the future.
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http://dx.doi.org/10.1364/OE.460766 | DOI Listing |
Cogn Neurodyn
December 2025
Department of Mathematics, Quaid-I-Azam University, Islamabad, Pakistan.
Algebraic structures are highly effective in designing symmetric key cryptosystems; however, if the key space is not sufficiently large, such systems become vulnerable to brute-force attacks. To address this challenge, our research focuses on enlarging the key space in symmetric key schemes by integrating the non-chain ring with a four-dimensional chaotic system. While chaotic maps offer significant potential for data processing, relying solely on them does not fully leverage their operational advantages.
View Article and Find Full Text PDFA high security physical layer encryption scheme for dual-mode orthogonal frequency division multiplexing with index modulation (DM-OFDM-IM) in magnetic induction communication is proposed. The scheme utilizes DM-OFDM-IM, where subcarriers within each subblock are divided into two groups, each modulated by distinct signal constellations. DM-OFDM-IM leverages the sequential information from the modulated constellation to transmit extra information, leading to a substantial enhancement in spectral efficiency.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Mathematics, College of Science, King Khalid, University, Abha, 61413, Saudi Arabia.
Algebraic structures play a vital role in securing important data. These structures are utilized to construct the non-linear components of block ciphers. Since constructing non-linear components through algebraic structures is crucial for the confusion aspects of encryption schemes, relying solely on these structures can result in limited key spaces.
View Article and Find Full Text PDFSensors (Basel)
January 2025
State Key Laboratory of Intelligent Vehicle Safety Technology, Chongqing 401133, China.
With the advancement of federated learning (FL), there is a growing demand for schemes that support multi-task learning on multi-modal data while ensuring robust privacy protection, especially in applications like intelligent connected vehicles. Traditional FL schemes often struggle with the complexities introduced by multi-modal data and diverse task requirements, such as increased communication overhead and computational burdens. In this paper, we propose a novel privacy-preserving scheme for multi-task federated split learning across multi-modal data (MTFSLaMM).
View Article and Find Full Text PDFSensors (Basel)
December 2024
Electrical Engineering and Computer Science (EECS), KTH Royal Institute of Technology, 10044 Stockholm, Sweden.
In the era of big data, advanced data processing devices and smart sensors greatly benefit us in many areas. As for each individual user, data sharing can be an essential part of the process of data collection and transmission. However, the issue of constant attacks on data privacy arouses huge concerns among the public.
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