Remote Sensing and Advanced Encryption Standard Using 256-Bit Key

Naman, Sumiran; Bhattacharyya, Sayari; Saha, Tufan

doi:10.1007/978-981-13-7403-6_18

Cited by 3 publications

(4 citation statements)

References 7 publications

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“…The advanced encryption standard (AES) or Rijndael, an encryption scheme, permits decryption of encrypted data by using a secret key thus implying that AES provides secure data storage (Kocabas & Soyata, 2015). The United States National Institute of Standards and Technology (NIST) accepted the AES algorithm in 2001 (Naman, Bhattacharyya, & Saha, 2018). AES became a standard of the Federal Government (Naman et al, 2018).…”

Section: Technical Measuresmentioning

confidence: 99%

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Challenges of implementation of data protection legislation in a South African context

Theys¹

2021

Proceedings of ‏The 11th International Conference on Research in Science and Technology

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South Africa has enacted its own information protection law, the Protection of Personal Information (POPI) Act, in 2013. 2018 saw the publication of the regulations. Neither the Act nor its regulations provide recommendations that assists organizations with achieving compliance. Furthermore, POPI impacts businesses from various sectors differently. The type of personal information stored and required for processing differs across organizations. Moreover, fines issued under the POPI Act could reach as high as 10 million Rand. This paper outlines a case study which explored within a single organization to identify what the challenges are that an organization could be faced with. The study is conducted throughout the organizational levels within a single organization. In the light of the imminent enforcement of the POPI Act, this paper highlights challenges that organizations could experience.

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Section: Technical Measuresmentioning

confidence: 99%

“…The United States National Institute of Standards and Technology (NIST) accepted the AES algorithm in 2001 (Naman, Bhattacharyya, & Saha, 2018). AES became a standard of the Federal Government (Naman et al, 2018). POPI does not mention encryption, it only requires reasonable technical measure be put in place to secure information.…”

Section: Technical Measuresmentioning

confidence: 99%

Challenges of implementation of data protection legislation in a South African context

Theys¹

2021

Proceedings of ‏The 11th International Conference on Research in Science and Technology

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“…Encryption is commonly acknowledged as a prevalent technique for protecting the confidentiality of remote sensing images during their transmission and storage. [4]. That is the use of symmetric or public key cryptographic algorithms to transform plaintext into ciphertext, which is then transmitted through a public channel to the recipient.…”

Section: Introductionmentioning

confidence: 99%

Content Protection Method for Remote Sensing Images Based on Deep Information Hiding

Luo,

Liu,

2024

Preprint

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To ensure the security of highly sensitive remote sensing images (RSI) during their transmission, it is essential to implement effective content security protection methods. Generally, secure distribution schemes for remote sensing images often employ cryptographic techniques. However, sending encrypted data exposes communication behavior, which poses significant security risks to the distribution of remote sensing images. Therefore, this paper introduces deep information hiding to achieve secure distribution of remote sensing images, which can serve as an effective alternative in certain specific scenarios. Specifically, the Deep Information Hiding for RSI Distribution (hereinafter referred to as DIH4RSID) based on encoder-decoder network architecture with Parallel Attention Mechanisms (PAM) by adversarial training was proposed. Our model is constructed with four main components: a preprocessing network (PN), an embedding network (EN), a revealing network (RN) and a discriminating network (DN). The PN module is primarily based on Inception to capture more details of RSI and targets of different scales. The PAM module obtains features in two spatial directions to realize feature enhancement and context information integration. The experimental results indicate that our proposed algorithm achieves relatively higher visual quality and secure level compared to related methods. Additionally, after extracting the concealed content from hidden images, the average classification accuracy is unaffected.

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“…Adopting a robust content security protection strategy is essential to safeguard the confidentiality of highly sensitive remote sensing data. Encryption is commonly acknowledged as a prevalent technique for protecting the confidentiality of remote sensing images during their transmission and storage [4]. That is, using symmetric or public key cryptographic algorithms to transform plaintext into ciphertext, then transmitting it through a public channel to the recipient.…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Images Secure Distribution Scheme Based on Deep Information Hiding

Luo,

Liu,

et al. 2024

Remote Sensing

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To ensure the security of highly sensitive remote sensing images (RSIs) during their distribution, it is essential to implement effective content security protection methods. Generally, secure distribution schemes for remote sensing images often employ cryptographic techniques. However, sending encrypted data exposes communication behavior, which poses significant security risks to the distribution of remote sensing images. Therefore, this paper introduces deep information hiding to achieve the secure distribution of remote sensing images, which can serve as an effective alternative in certain specific scenarios. Specifically, the Deep Information Hiding for RSI Distribution (hereinafter referred to as DIH4RSID) based on an encoder–decoder network architecture with Parallel Attention Mechanism (PAM) by adversarial training is proposed. Our model is constructed with four main components: a preprocessing network (PN), an embedding network (EN), a revealing network (RN), and a discriminating network (DN). The PN module is primarily based on Inception to capture more details of RSIs and targets of different scales. The PAM module obtains features in two spatial directions to realize feature enhancement and context information integration. The experimental results indicate that our proposed algorithm achieves relatively higher visual quality and secure level compared to related methods. Additionally, after extracting the concealed content from hidden images, the average classification accuracy is unaffected.

show abstract

Remote Sensing and Advanced Encryption Standard Using 256-Bit Key

Cited by 3 publications

References 7 publications

Challenges of implementation of data protection legislation in a South African context

Challenges of implementation of data protection legislation in a South African context

Content Protection Method for Remote Sensing Images Based on Deep Information Hiding

Remote Sensing Images Secure Distribution Scheme Based on Deep Information Hiding

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