A framework for GPU-accelerated AES-XTS encryption in mobile devices

Alomari, Mohammad Ahmed; Samsudin, Khairulmizam

doi:10.1109/tencon.2011.6129080

Cited by 12 publications

(6 citation statements)

References 12 publications

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“…Nevertheless, it executes rather slowly and requires additional attention to protect the similar key pair. A GPU-enhanced structure was detailed in [22] for loading data in mobile devices by generating encrypted data using the XTS-AES encryption process. This encryption scheme approaches security through the loading of data of very large size and processes a very large quantity of data in parallel.…”

Section: A Cryptography and Patterningmentioning

confidence: 99%

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Unified GPU Technique to Boost Confidentiality, Integrity and Trim Data Loss in Big Data Transmission

Bhattacharjee¹,

Rahim

Watada

et al. 2020

IEEE Access

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Data integrity, confidentiality and data loss are the issues that arise during transmission because of the use of an inadequate security scheme. These issues become particularly critical for big data transmission due to its own individual overhead causes. Moreover, multiple executions of distinct security algorithms for maintaining confidentiality and integrity reduce throughput and add a large number of additional bits as security overhead that hampers the robustness against data loss. Conversely, an efficient compression technique minimizes data confidentiality, as it eliminates redundant data during compression. Contemporary studies shows the lack of security policies for solving the mentioned issues in a combinatorial manner. The current study proposes an innovative integrated technique to collectively addresses the above security issues. It increases confidentiality and offers a backup for accidental data loss by combining the simplified data encryption standard (SDES) and an advanced pattern generation technique that uses a unique pattern generation table. A novel dual round of error control technique has been introduced to maximize data integrity by considering an arbitrary number of transmission errors. A new compression technique is adopted to enhance the robustness against data loss along with high compression efficiency and resistance against transmission errors. Confidentiality and integrity are further enhanced by integrating advanced audio steganography that uses a distinctive sample selection for hiding bits. Additionally, the implementation of the proposed innovative integrated technique in the graphics processing unit (GPU) environment increases the execution speed and reduces time complexity with extended parallel processing power. Furthermore, the application of a GPU enhances the execution speed at least 28-fold compared to the CPU performance. Experiments are performed using the standard Calgary Corpuses, text files (sized up to 1 TB), and audio files to validate the objectives. The proposed method offers a higher signal-to-noise ratio (SNR), entropy, and avalanche effect (AE) and lower amplitude difference (AD), and uncorrectable error rate (UER) as well as a lower percentage of information loss (IL), which substantiates its potential to offer higher data confidentiality and integrity. The capacity to reduce the computational complexity is further measured with compression ratio (CR) and throughput. The results further depicts the method's superiority in offering confidentiality and integrity over contemporary approaches. INDEX TERMS SDES, pattern string generation and the incorporation technique, dual round XOR operations to control transmission error, data compression, LSB-based audio steganography, GPU-oriented integrated technique, signal-to-noise ratio (SNR), entropy, avalanche effect (AE), amplitude difference (AD), uncorrectable error rate (UER), percentage of information loss (IL), compression ratio (CR) and throughput.

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Section: A Cryptography and Patterningmentioning

confidence: 99%

“…)Sam pi = 1; (P1.20)End for (P1 21). for (int j = 4; j ≤ 7; j + +) (P1 22). if (Sam pj = 1) End ifIn Pass 4.1, steps (P1.1-P1.5) consider the changes made if the third and fifth bits of Sam p are both 1, and the fourth bit changes from 0 to 1 during the inclusion of a compressed bit at the fourth position.…”

mentioning

confidence: 99%

Unified GPU Technique to Boost Confidentiality, Integrity and Trim Data Loss in Big Data Transmission

Bhattacharjee¹,

Rahim

Watada

et al. 2020

IEEE Access

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“…Inherently, GPUs are used to offload computationally intensive tasks from the CPU. Some of these tasks may require the processing or handling of confidential information, such as cryptographic keys [3], private network traffic [4] or financial data [5]. GPUs are also marketed as cloud computing services (GPU-as-a-service) where infrastructure and resources are shared between multiple tenants.…”

Section: Introductionmentioning

confidence: 99%

Data remanence and digital forensic investigation for CUDA Graphics Processing Units

Bellekens

Paul

Irvine

et al. 2015

2015 IFIP/IEEE International Symposium on Integrated Network Management (IM)

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This paper investigates the practicality of memory attacks on commercial Graphics Processing Units (GPUs). With recent advances in the performance and viability of using GPUs for various highly-parallelised data processing tasks, a number of security challenges are raised. Unscrupulous software running subsequently on the same GPU, either by the same user, or another user, in a multiuser system, may be able to gain access to the contents of the GPU memory. This contains data from previous program executions. In certain use-cases, where the GPU is used to offload intensive parallel processing such as pattern matching for an intrusion detection system, financial systems, or cryptographic algorithms, it may be possible for the GPU memory to contain privileged data, which would ordinarily be inaccessible to an unprivileged application running on the host computer. With GPUs potentially yielding access to confidential information, existing research in the field is built upon, to investigate the practicality of extracting data from global, shared and texture memory, and retrieving this data for further analysis. These techniques are also implemented on various GPUs using three different Nvidia CUDA versions. A novel methodology for digital forensic examination of GPU memory for remanent data is then proposed, along with some suggestions and considerations towards countermeasures and anti-forensic techniques.

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“…And also the needs to process cryptography on these devices exist. For this reason, accelerating cryptographic processing while using a many-core processor with scalable architecture is expected to constitute one solution for highspeed and scalable cryptographic processing [3]- [5]. Highspeed cryptographic processing accelerated by a many-core architecture such as that of GPGPUs will resolve problems that cannot be solved by AES-NI because those GPGPU accelerations based on a software implementation can accept any cryptographic algorithm.…”

Section: Introductionmentioning

confidence: 99%

HiCrypt: A Specialized Translator for Symmetric Block Cipher and GPGPU

Iwai

Nishikawa

Kurokawa

2013

IEICE Trans. Inf. & Syst.

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SUMMARYMany-core computer systems with GPUs are coming into mainstream use from high-end computing, including supercomputers, to embedded processors. Consequently, the implementation of cryptographic methods on GPGPU is also becoming popular because of such systems' performance. However, many factors affect the performance of GPUs. To cope with this problem, we developed a new translator, HiCrypt, which can generate an optimized GPGPU program written in both of CUDA and OpenCL from a cipher program written in standard C language with directives. Users must annotate only variables and an encoding/decoding function, which are characteristics of cipher programs, with directives. To evaluate the translator, five representative cipher programs are translated into CUDA and OpenCL programs by the translator. Generated programs perform high throughput almost identical to hand optimized programs for all five cipher programs. HiCrypt will contribute to development and evaluate of new and various symmetric block ciphers using GPGPU.

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A framework for GPU-accelerated AES-XTS encryption in mobile devices

Cited by 12 publications

References 12 publications

Unified GPU Technique to Boost Confidentiality, Integrity and Trim Data Loss in Big Data Transmission

Unified GPU Technique to Boost Confidentiality, Integrity and Trim Data Loss in Big Data Transmission

Data remanence and digital forensic investigation for CUDA Graphics Processing Units

HiCrypt: A Specialized Translator for Symmetric Block Cipher and GPGPU

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