A Survey of Techniques for Improving Security of GPUs

Graphics processing unit (GPU), although a powerful performance-booster, also has many security vulnerabilities. Due to these, the GPU can act as a safe-haven for stealthy malware and the weakest `link' in the security `chain'. In this paper, we present a survey of techniques for analyzing and improving GPU security. We classify the works on key attributes to highlight their similarities and differences. More than informing users and researchers about GPU security techniques, this survey aims to increase their awareness about GPU security vulnerabilities and potential countermeasures

Cryptographic algorithm acceleration using CUDA enabled GPUs in typical system configurations

The need to encrypt data is becoming more and more necessary. As the size of datasets continues to grow, the speed of encryption must increase to keep up or it will become a bottleneck. CUDA GPUs have been shown to offer performance improvements versus conventional CPUs for some data-intensive problems. This thesis evaluates the applicability of CUDA GPUs in accelerating the execution of cryptographic algorithms, which are increasingly used for growing amounts of data and thus will require significantly faster encryption and hashing throughput. Specifically, the CUDA environment was used to implement and experiment with three distinct cryptographic algorithms -- AES, SHA-2, and Keccak -- in order to show the applicability for various cryptographic algorithm classes. They were implemented in a system that emulates the conditions present in a real world environment, and the effects of offloading these tasks from the CPU to the GPU were assessed. Speedups up to 2.6x relative to the CPU were seen for single-kernel AES, but SHA-2 and Keccak did not perform as well as on the GPU as on the CPU. Multi-kernel AES saw speedups over single-kernel AES up to 1.4x, 1.65x, and 1.8x for two, three, and four kernels, respectively. This translates to speedups between 3.6x and 4.7x over CPU implementations of AES. Introducing a CPU load had a minimal effect on throughput whereas a GPU load was seen to decrease throughput by as much as 4%. Overall, CUDA GPUs appear to have potential for improving encryption throughputs if a parallelizable algorithm is selected

Accelerating NTRUEncrypt for in-browser cryptography utilising graphical processing units and WebGL

One of the challenges encryption faces is it is computationally intensive and therefore slow, it is vital to find faster methods to accelerate modern encryption algorithms to keep performance high whilst also preserving information security. Users often do not want to wait for applications to become responsive, applications on limited devices such as mobiles often compromise security in order to keep execution times quick. Often they use algorithms and key sizes which are not considered cryptographically secure in order to maintain a smooth user experience. Emerging approaches have begun using a devices Graphics Processing Unit (GPU) to offload some of the computational burden from the Central Processing Unit (CPU) in an effort to parallelize and accelerate the encryption algorithms. Programming for a GPU often involves the use of CUDA or OpenCL programming, however these approaches are platform dependant. This research focuses on utilizing a GPU to perform in-browser cryptography using WebGL and JavaScript. This allows any GPU-enabled device capable of launching an OpenGL compatible browser to perform GPU accelerated cryptography. A GPU based implementation of the NTRUEncrypt algorithm was created and tested against a CPU based version on a range of hardware devices with results, challenges and limitations discussed

Survey and future trends of efficient cryptographic function implementations on GPGPUs

Many standard cryptographic functions are designed to benefit from hardware specific implementations. As a result, there have been a large number of highly efficient ASIC and FPGA hardware based implementations of standard cryptographic functions. Previously, hardware accelerated devices were only available to a limited set of users. General Purpose Graphic Processing Units (GPGPUs) have become a standard consumer item and have demonstrated orders of magnitude performance improvements for general purpose computation, including cryptographic functions. This paper reviews the current and future trends in GPU technology, and examines its potential impact on current cryptographic practice

Encrypting video and image streams using OpenCL code on- demand

La cantidad de información multimedia que se transmite a través de la web es muy alta y continua incrementándose. Generalmente, este tipo de datos no se los protege correctamente, dado que los usuarios no aprecian la cantidad de información que las imágenes y videos pueden contener. En este trabajo, presentamos una arquitectura para manejar de manera segura, canales de transmisión multimedia. La idea es encriptar o codificar imágenes y videos de una forma eficiente y dinámica. Al mismo tiempo, estos datos pueden ser mejorados aplicando un procesamiento en tiempo real. Lo novedoso de esta propuesta es la utilización en tiempo real de código bajo-demanda en paralelo escrito en OpenCL. Los algoritmos y estructura de datos son conocidos por los participantes de la comunicación, solo cuando esta comienza, lo que supone incrementa la robustez frente a posibles ataques. En el trabajo desarrollamos una descripción completa de la propuesta y varias pruebas de rendimiento con diferentes algoritmos.The amount of multimedia information transmitted through the web is very high and increasing. Generally, this kind of data is not correctly protected, since users do not appreciate the amount of information that images and videos may contain. In this work, we present architecture for managing safely multimedia transmission channels. The idea is to encrypt or encode images and videos in an efficient and dynamic way. At the same time, these media could be enhanced applying a real-time image process. The main novelty of the proposal is the application of on-demand parallel code written in OpenCL. The algorithms and data structure are known by the parties only at communication time, what we suppose increases the robustness against possible attacks. We conducted a complete description of the proposal and several performance tests with different known algorithms.Fil: D'amato, Juan Pablo. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Venere, Marcelo Javier. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados; Argentina. Comisión Nacional de Energía Atómica; Argentin