2023
DOI: 10.1109/access.2023.3235201
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Generative Adversarial Networks for DNA Storage Channel Simulator

Abstract: DNA data storage systems have rapidly developed with novel error-correcting techniques, random access algorithms, and query systems. However, designing an algorithm for DNA storage systems is challenging, mainly due to the unpredictable nature of errors and the extremely high price of experiments. Thus, a simulator is of interest that can imitate the error statistics of a DNA storage system and replace the experiments in developing processes. We introduce novel generative adversarial networks that learn DNA st… Show more

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Cited by 5 publications
(2 citation statements)
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“…These certainly affect the ability to store data securely. In this context, it is preferred to use DNA-based cryptosystems along with advanced AI technologies that allow encryption and decryption algorithms executed with simple operations, thus improving significant computational resources [240]. Moreover, when dealing with untrusted third parties, masking encrypted confidential data in an image before storage with a DNA cryptosystem designed in [196] and distributing them with multiple replicas on untrusted third parties/clouds as suggested in [213] could be promising solutions not only to secure data storage but also to improve IoT network resources.…”
Section: E Countermeasures 1) Unreliable Communicationmentioning
confidence: 99%
“…These certainly affect the ability to store data securely. In this context, it is preferred to use DNA-based cryptosystems along with advanced AI technologies that allow encryption and decryption algorithms executed with simple operations, thus improving significant computational resources [240]. Moreover, when dealing with untrusted third parties, masking encrypted confidential data in an image before storage with a DNA cryptosystem designed in [196] and distributing them with multiple replicas on untrusted third parties/clouds as suggested in [213] could be promising solutions not only to secure data storage but also to improve IoT network resources.…”
Section: E Countermeasures 1) Unreliable Communicationmentioning
confidence: 99%
“…assumption unrealistic. To account for these dependencies, a first set of simulators have employed Deep-Learning (DL) based approaches, such as DeepSimulator [6], [7], which utilizes Deep Neural Networks (DNNs), or the approach of [8] which uses Generative Adversarial Networks (GANs). However, these simulators are trained for specific sequence lengths, and the training process is computationally expensive.…”
Section: Introductionmentioning
confidence: 99%