Fine-grained fault-tolerance : reliability as a fungible resource

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.Includes bibliographical references (p. 131-134).The traditional design of logic circuits, based on reliable components, is incompatible with the next generation of devices relying on fewer resources and subject to high rates of soft errors. These allow a trade-off between failure probability and their space and power consumption. Using this, we show that reliability can be a fungible resource, interconvertible with other physical resources in multiple, unusual ways, via fault-tolerant architectures. This thesis investigates the potentialities offered by a fault-tolerant design in devices whose reliability is limited by shrinking resources. Surprisingly, we find that an appropriate use of structured redundancy could lead to more efficient components. The performance of a fine-grained multiplexed design can indeed be systematically evaluated in terms of resource savings and reliability improvement. This analysis is applied to characterize technologies at the nano scale, such as molecular electronics, which may benefit enormously by fault-tolerant designs.by François Impens.S.M

Quantum Computing: Lecture Notes

This is a set of lecture notes suitable for a Master's course on quantum computation and information from the perspective of theoretical computer science. The first version was written in 2011, with many extensions and improvements in subsequent years. The first 10 chapters cover the circuit model and the main quantum algorithms (Deutsch-Jozsa, Simon, Shor, Hidden Subgroup Problem, Grover, quantum walks, Hamiltonian simulation and HHL). They are followed by 3 chapters about complexity, 4 chapters about distributed ("Alice and Bob") settings, and a final chapter about quantum error correction. Appendices A and B give a brief introduction to the required linear algebra and some other mathematical and computer science background. All chapters come with exercises, with some hints provided in Appendix C.Comment: 184 pages. Version 2: added a new chapter about QMA and local Hamiltonian, more exercises in several chapters, and some small corrections/clarification

Quantum Computing: Lecture Notes

This is a set of lecture notes suitable for a Master's course on quantum computation and information from the perspective of theoretical computer science. The first version was written in 2011, with many extensions and improvements in subsequent years. The first 10 chapters cover the circuit model and the main quantum algorithms (Deutsch-Jozsa, Simon, Shor, Hidden Subgroup Problem, Grover, quantum walks, Hamiltonian simulation and HHL). They are followed by 2 chapters about complexity, 4 chapters about distributed ("Alice and Bob") settings, and a final chapter about quantum error correction. Appendices A and B give a brief introduction to the required linear algebra and some other mathematical and computer science background. All chapters come with exercises, with some hints provided in Appendix C

Towards Improved Homomorphic Encryption for Privacy-Preserving Deep Learning

Mención Internacional en el título de doctorDeep Learning (DL) has supposed a remarkable transformation for many fields, heralded by some as a new technological revolution. The advent of large scale models has increased the demands for data and computing platforms, for which cloud computing has become the go-to solution. However, the permeability of DL and cloud computing are reduced in privacy-enforcing areas that deal with sensitive data. These areas imperatively call for privacy-enhancing technologies that enable responsible, ethical, and privacy-compliant use of data in potentially hostile environments. To this end, the cryptography community has addressed these concerns with what is known as Privacy-Preserving Computation Techniques (PPCTs), a set of tools that enable privacy-enhancing protocols where cleartext access to information is no longer tenable. Of these techniques, Homomorphic Encryption (HE) stands out for its ability to perform operations over encrypted data without compromising data confidentiality or privacy. However, despite its promise, HE is still a relatively nascent solution with efficiency and usability limitations. Improving the efficiency of HE has been a longstanding challenge in the field of cryptography, and with improvements, the complexity of the techniques has increased, especially for non-experts. In this thesis, we address the problem of the complexity of HE when applied to DL. We begin by systematizing existing knowledge in the field through an in-depth analysis of state-of-the-art for privacy-preserving deep learning, identifying key trends, research gaps, and issues associated with current approaches. One such identified gap lies in the necessity for using vectorized algorithms with Packed Homomorphic Encryption (PaHE), a state-of-the-art technique to reduce the overhead of HE in complex areas. This thesis comprehensively analyzes existing algorithms and proposes new ones for using DL with PaHE, presenting a formal analysis and usage guidelines for their implementation. Parameter selection of HE schemes is another recurring challenge in the literature, given that it plays a critical role in determining not only the security of the instantiation but also the precision, performance, and degree of security of the scheme. To address this challenge, this thesis proposes a novel system combining fuzzy logic with linear programming tasks to produce secure parametrizations based on high-level user input arguments without requiring low-level knowledge of the underlying primitives. Finally, this thesis describes HEFactory, a symbolic execution compiler designed to streamline the process of producing HE code and integrating it with Python. HEFactory implements the previous proposals presented in this thesis in an easy-to-use tool. It provides a unique architecture that layers the challenges associated with HE and produces simplified operations interpretable by low-level HE libraries. HEFactory significantly reduces the overall complexity to code DL applications using HE, resulting in an 80% length reduction from expert-written code while maintaining equivalent accuracy and efficiency.El aprendizaje profundo ha supuesto una notable transformación para muchos campos que algunos han calificado como una nueva revolución tecnológica. La aparición de modelos masivos ha aumentado la demanda de datos y plataformas informáticas, para lo cual, la computación en la nube se ha convertido en la solución a la que recurrir. Sin embargo, la permeabilidad del aprendizaje profundo y la computación en la nube se reduce en los ámbitos de la privacidad que manejan con datos sensibles. Estas áreas exigen imperativamente el uso de tecnologías de mejora de la privacidad que permitan un uso responsable, ético y respetuoso con la privacidad de los datos en entornos potencialmente hostiles. Con este fin, la comunidad criptográfica ha abordado estas preocupaciones con las denominadas técnicas de la preservación de la privacidad en el cómputo, un conjunto de herramientas que permiten protocolos de mejora de la privacidad donde el acceso a la información en texto claro ya no es sostenible. Entre estas técnicas, el cifrado homomórfico destaca por su capacidad para realizar operaciones sobre datos cifrados sin comprometer la confidencialidad o privacidad de la información. Sin embargo, a pesar de lo prometedor de esta técnica, sigue siendo una solución relativamente incipiente con limitaciones de eficiencia y usabilidad. La mejora de la eficiencia del cifrado homomórfico en la criptografía ha sido todo un reto, y, con las mejoras, la complejidad de las técnicas ha aumentado, especialmente para los usuarios no expertos. En esta tesis, abordamos el problema de la complejidad del cifrado homomórfico cuando se aplica al aprendizaje profundo. Comenzamos sistematizando el conocimiento existente en el campo a través de un análisis exhaustivo del estado del arte para el aprendizaje profundo que preserva la privacidad, identificando las tendencias clave, las lagunas de investigación y los problemas asociados con los enfoques actuales. Una de las lagunas identificadas radica en el uso de algoritmos vectorizados con cifrado homomórfico empaquetado, que es una técnica del estado del arte que reduce el coste del cifrado homomórfico en áreas complejas. Esta tesis analiza exhaustivamente los algoritmos existentes y propone nuevos algoritmos para el uso de aprendizaje profundo utilizando cifrado homomórfico empaquetado, presentando un análisis formal y unas pautas de uso para su implementación. La selección de parámetros de los esquemas del cifrado homomórfico es otro reto recurrente en la literatura, dado que juega un papel crítico a la hora de determinar no sólo la seguridad de la instanciación, sino también la precisión, el rendimiento y el grado de seguridad del esquema. Para abordar este reto, esta tesis propone un sistema innovador que combina la lógica difusa con tareas de programación lineal para producir parametrizaciones seguras basadas en argumentos de entrada de alto nivel sin requerir conocimientos de bajo nivel de las primitivas subyacentes. Por último, esta tesis propone HEFactory, un compilador de ejecución simbólica diseñado para agilizar el proceso de producción de código de cifrado homomórfico e integrarlo con Python. HEFactory es la culminación de las propuestas presentadas en esta tesis, proporcionando una arquitectura única que estratifica los retos asociados con el cifrado homomórfico, produciendo operaciones simplificadas que pueden ser interpretadas por bibliotecas de bajo nivel. Este enfoque permite a HEFactory reducir significativamente la longitud total del código, lo que supone una reducción del 80% en la complejidad de programación de aplicaciones de aprendizaje profundo que usan cifrado homomórfico en comparación con el código escrito por expertos, manteniendo una precisión equivalente.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidenta: María Isabel González Vasco.- Secretario: David Arroyo Guardeño.- Vocal: Antonis Michala

LIPIcs, Volume 274, ESA 2023, Complete Volume

LIPIcs, Volume 274, ESA 2023, Complete Volum