A parallel block-based encryption schema for digital images using reversible cellular automata

AbstractWe propose a novel images encryption schema based on reversible one-dimensional cellular automata. Contrasting to the sequential operating mode of several existing approaches, the proposed one is fully parallelizable since the encryption/decryption tasks can be executed using multiple processes running independently for the same single image. The parallelization is made possible by defining a new RCA-based construction of an extended pseudorandom permutation that takes a nonce as a supplementary parameter. The defined PRP exploit the chaotic behavior and the high initial condition's sensitivity of the RCAs to ensure perfect cryptographic security properties. Results of various experiments and analysis show that high security and execution performances can be achieved using the approach, and furthermore, it provides the ability to perform a selective area decryption since any part of the ciphered-image can be deciphered independently from others, which is very useful for real time applications

Modelo de encriptación simétrica basada en atractores caóticos

Context: the increase in the processing capacity of machines and developments in combinatorial search algorithms reduces the time required to decipher the information fraudulently. Bear in mind this, there is a need to generate new ways of encoding information for secure transmission.Method: in this paper a symmetrical and extensible model for digital communications encryption is presented, taking advantage of the chaos generated by nonlinear dynamic systems.Results: the developed model proved to encrypt messages in time synchronization, encryption and decryption less than 1 ms with an entropy higher than 6 using the Rössler attractor for its implementation.Conclusions: the algorithm is presented as an alternative to traditional algorithms demonstrating greater efficiency in the management of computing resources and raises the groundwork for continuing their study on the interested academic community due to the variety of dynamical systems nonlinear.Contexto: El aumento en la capacidad de procesamiento de las máquinas y los desarrollos en los algoritmos de búsqueda combinatoria disminuyen el tiempo necesario para descifrar fraudulentamente la información; por esta razón se plantea la necesidad de generar nuevas formas de codificar la  información para su transmisión segura.Método: En este artículo se presenta un modelo de encriptación simétrico extensible para comunicaciones digitales, aprovechando el caos generado por sistemas dinámicos no lineales.  Resultados: El modelo desarrollado demostró estar en la capacidad de encriptar mensajes en tiempos de sincronización, encriptación y desencriptación inferiores a 1 ms con una entropía superior a 6 usando el atractor de Rossler para su implementación.Conclusiones: El algoritmo se presenta como una alternativa a los algoritmos tradicionales de combinatoria demostrando una mayor eficiencia en la gestión de recursos computacionales y plantea las bases para su continuar con su estudio en la comunidad académica interesada, debido a la variedad de los sistemas dinámicos no lineales.

Linking Climate Change and Socio-economic Impact for Long-term Urban Growth in Three Mega-cities

Urbanization has become a global trend under the impact of population growth, socio-economic development, and globalization. However, the interactions between climate change and urban growth in the context of economic geography are unclear due to missing links in between the recent planning megacities. This study aims to conduct a multi-temporal change analysis of land use and land cover in New York City, City of London, and Beijing using a cellular automata-based Markov chain model collaborating with fuzzy set theory and multi-criteria evaluation to predict the city\u27s future land use changes for 2030 and 2050 under the background of climate change. To determine future natural forcing impacts on land use in these megacities, the study highlighted the need for integrating spatiotemporal modeling analyses, such as Statistical Downscale Modeling (SDSM) driven by climate change, and geospatial intelligence techniques, such as remote sensing and geographical information system, in support of urban growth assessment. These SDSM findings along with current land use policies and socio-economic impact were included as either factors or constraints in a cellular automata-based Markov Chain model to simulate and predict land use changes in megacities for 2030 and 2050. Urban expansion is expected in these megacities given the assumption of stationarity in urban growth process, although climate change impacts the land use changes and management. More land use protection should be addressed in order to alleviate the impact of climate change