Enhanced fully homomorphic encryption scheme using modified key generation for cloud environment

Fully homomorphic encryption (FHE) is a special class of encryption that allows performing unlimited mathematical operations on encrypted data without decrypting it. There are symmetric and asymmetric FHE schemes. The symmetric schemes suffer from the semantically security property and need more performance improvements. While asymmetric schemes are semantically secure however, they pose two implicit problems. The first problem is related to the size of key and ciphertext and the second problem is the efficiency of the schemes. This study aims to reduce the execution time of the symmetric FHE scheme by enhancing the key generation algorithm using the Pick-Test method. As such, the Binary Learning with Error lattice is used to solve the key and ciphertext size problems of the asymmetric FHE scheme. The combination of enhanced symmetric and asymmetric algorithms is used to construct a multi-party protocol that allows many users to access and manipulate the data in the cloud environment. The Pick-Test method of the Sym-Key algorithm calculates the matrix inverse and determinant in one instance requires only n-1 extra multiplication for the calculation of determinant which takes 0(N3) as a total cost, while the Random method in the standard scheme takes 0(N3) to find matrix inverse and 0(N!) to calculate the determinant which results in 0(N4) as a total cost. Furthermore, the implementation results show that the proposed key generation algorithm based on the pick-test method could be used as an alternative to improve the performance of the standard FHE scheme. The secret key in the Binary-LWE FHE scheme is selected from {0,1}n to obtain a minimal key and ciphertext size, while the public key is based on learning with error problem. As a result, the secret key, public key and tensored ciphertext is enhanced from logq , 0(n2log2q) and ((n+1)n2log2q)2log q to n, (n+1)2log q and (n+1)2log q respectively. The Binary-LWE FHE scheme is a secured but noise-based scheme. Hence, the modulus switching technique is used as a noise management technique to scale down the noise from e and c to e/B and c/B respectively thus, the total cost for noise management is enhanced from 0(n3log2q) to 0(n2log q) . The Multi-party protocol is constructed to support the cloud computing on Sym-Key FHE scheme. The asymmetric Binary-LWE FHE scheme is used as a small part of the protocol to verify the access of users to any resource. Hence, the protocol combines both symmetric and asymmetric FHE schemes which have the advantages of efficiency and security. FHE is a new approach with a bright future in cloud computing

Metodología para la extracción distribuida de imágenes forenses incrementales de dispositivos móviles con sistema operativo Android a través de redes WIFI

Los dispositivos móviles, como los smartphones y las tablets, se convirtieron desde hace algunos años en instrumentos no sólo para uso doméstico o personal, sino como herramienta de trabajo. Al mismo tiempo comenzaron a ser también susceptibles para los delitos informáticos, ya sea como facilitador o como puente para llegar a información corporativa. Se han analizado las arquitecturas de las diferentes versiones de Android para conocer sus diferencias y poder hacer una copia exacta del contenido del dispositivo móvil. También se analizó un método apropiado de cifrado para la imagen extraída. Generalmente las copias exactas de los dispositivos se hacen de forma completa cada vez, pero en este trabajo se prueba la copia exacta de la información nueva o modificada en el dispositivo móvil, con el fin que en cada extracción los archivos sean de tamaño pequeño y facilitar el transporte vía WiFi. Y, por último, la validación de la metodología propuesta en un ambiente controlado que la confronta con la metodología tradicional alámbrica. Los resultados se muestran por cada uno de los componentes analizados, donde se puede concluir que es muy posible que se adopte esta metodologíaMobile devices, such as smartphones and tablets, have for some years become instruments not only for domestic or personal use, but as a work tool. At the same time, they also began to be susceptible to computer crimes, either as a facilitator or as a bridge to reach corporate information. The architectures of the different versions of Android have been analyzed to know their differences and to be able to make an exact copy of the content of the mobile device. An appropriate encryption method for the extracted image was also analyzed. Generally, the exact copies of the devices are made completely every time, but in this work the exact copy of the new or modified information on the mobile device is tested, so that in each extraction the files are of small size and facilitate the transport via WiFi. And, finally, the validation of the proposed methodology in a controlled environment that confronts it with the traditional wired methodology. The results are shown for each of the components analyzed, where it can be concluded that it is very possible that this methodology is adopted

Dynamically reconfigurable bio-inspired hardware

During the last several years, reconfigurable computing devices have experienced an impressive development in their resource availability, speed, and configurability. Currently, commercial FPGAs offer the possibility of self-reconfiguring by partially modifying their configuration bitstream, providing high architectural flexibility, while guaranteeing high performance. These configurability features have received special interest from computer architects: one can find several reconfigurable coprocessor architectures for cryptographic algorithms, image processing, automotive applications, and different general purpose functions. On the other hand we have bio-inspired hardware, a large research field taking inspiration from living beings in order to design hardware systems, which includes diverse topics: evolvable hardware, neural hardware, cellular automata, and fuzzy hardware, among others. Living beings are well known for their high adaptability to environmental changes, featuring very flexible adaptations at several levels. Bio-inspired hardware systems require such flexibility to be provided by the hardware platform on which the system is implemented. In general, bio-inspired hardware has been implemented on both custom and commercial hardware platforms. These custom platforms are specifically designed for supporting bio-inspired hardware systems, typically featuring special cellular architectures and enhanced reconfigurability capabilities; an example is their partial and dynamic reconfigurability. These aspects are very well appreciated for providing the performance and the high architectural flexibility required by bio-inspired systems. However, the availability and the very high costs of such custom devices make them only accessible to a very few research groups. Even though some commercial FPGAs provide enhanced reconfigurability features such as partial and dynamic reconfiguration, their utilization is still in its early stages and they are not well supported by FPGA vendors, thus making their use difficult to include in existing bio-inspired systems. In this thesis, I present a set of architectures, techniques, and methodologies for benefiting from the configurability advantages of current commercial FPGAs in the design of bio-inspired hardware systems. Among the presented architectures there are neural networks, spiking neuron models, fuzzy systems, cellular automata and random boolean networks. For these architectures, I propose several adaptation techniques for parametric and topological adaptation, such as hebbian learning, evolutionary and co-evolutionary algorithms, and particle swarm optimization. Finally, as case study I consider the implementation of bio-inspired hardware systems in two platforms: YaMoR (Yet another Modular Robot) and ROPES (Reconfigurable Object for Pervasive Systems); the development of both platforms having been co-supervised in the framework of this thesis