Bernstein has since proposed that the name Curve25519 be used comparison of cryptography libraries the underlying curve, and the name X25519 for the DH function. This base point has order .
Montgomery ladder for ECDH, using only XZ coordinates. Curve25519 is constructed such that it avoids many potential implementation pitfalls. By design, it is immune to timing attacks and it accepts any 32-byte string as a valid public key and does not require validation. The curve is birationally equivalent to a twisted Edwards curve used in Ed25519 signature scheme. Curve25519 was first released by Daniel J.
Bernstein in 2005, but interest increased considerably after 2013 when it was discovered that the NSA had implemented a backdoor into Dual_EC_DRBG. I no longer trust the constants. I believe the NSA has manipulated them through their relationships with industry. Since then, Curve25519 has become the de facto alternative to P-256, and is used in a wide variety of applications. Starting in 2014, OpenSSH defaults to Curve25519-based ECDH.
In 2017, NIST announced that Curve25519 and Curve448 would be added to Special Publication 800-186, which specifies approved elliptic curves for use by the US Federal Government. Both are described in RFC 7748. Exclusive key exchange in OpenSSH 6. A state-of-the-art Diffie-Hellman function by Daniel J.
Bernstein”My curve25519 library computes the Curve25519 function at very high speed. The library is in the public domain. Faster addition and doubling on elliptic curves. A Few Thoughts on Cryptographic Engineering: The Many Flaws of Dual_EC_DRBG”. The NSA Is Breaking Most Encryption on the Internet – Schneier on Security”. Transition Plans for Key Establishment Schemes”.
Archived from the original on 2015-05-20. Add support for ECDHE with X25519. Gajim plugin for OMEMO Multi-End Message and Object Encryption”. CS1 maint: Explicit use of et al. OpenBSD Moving Towards Signed Packages — Based On D. OpenSSH No Longer Has To Depend On OpenSSL”.
How does Peerio implement end-to-end encryption? Virgil crypto uses Curve25519 for ECIES by default”. Hidden categories: CS1 maint: Explicit use of et al. Introduces fundamental concepts of computer science and computational thinking. Introduces the design and implementation of computer programs. Includes an introduction to program structure, data types, and object-oriented design. Prerequisite: a minimum grade of 2.
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0 in either TMATH 116, TMATH 120, TMATH 121, or MATH 120, a score of 154-163 on MPT-AS test, or a score of 2 on AP exam. Develops fundamental concepts and techniques for analysis, design, and implementation of computer programs using an object-oriented language. Includes recursive techniques and use of abstract data types. 0 in either TCSS 142 or CSE 142.
Provides a practicum in program design and development. Programming practice on a medium-scale, object-oriented application, consolidating prior programming principles and expanding knowledge of application design. Introduces definitions and tools for reasoning about discrete mathematical objects useful for computer professionals, including set theory, propositions and predicates, Boolean algebra, sequences, enumeration, algorithms, methods of proof, and relations. Covers advanced topics in discrete mathematics useful for computing professionals, including basic counting techniques, discrete probability, recurrence relations, graphs, trees, and models of computation such as finite state machines and Turing machines. 0 in TCSS 321 and either TMATH 110 or TMATH 390. Analyzes social, political, and ethical implications of computer and information technologies.
Covers Western ethical theories, professional ethics, and diverse topics in computer ethics. Emphasizes writing and the construction of ethical arguments. Introduces C as a language for exploring low-level machine characteristics and interacting with operating system services. Includes bit models for numeric data, pointers, arrays and structures, memory allocation, development of multiple file programs, libraries, system calls, and tools for compiling and linking.
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Covers data structures and classical algorithms with an emphasis on implementing them in high-level programming languages. Includes sequential and linked lists, binary trees, heaps, B-trees, hash tables, graphs, and algorithms for searching and sorting. Concentrates on developing implementations, understanding their performance, and estimating their potential effectiveness in applications. Develops competencies associated with problem-solving, algorithms, and computational models. Explores algorithms analysis and design, and computational complexity.
Includes efficient algorithms, models of computation, correctness, time and space complexity, NP-complete problems, and undecidable problems. Covers how to build quality software using standard development practices and representations. Includes writing and using requirements, designing and representing computational units, rigorous program testing, reviews and inspections, and working effectively in teams. Develops the hardware basis for computing systems, and the relationship between hardware and software. Covers number representations, digital logic, machine organization, instruction set architecture, assembly language, and translation of high-level languages into machine instructions. Covers the microarchitecture level of machine design and advanced architecture features for perform enhancement. Introduces fundamental programming language concepts common to all programming languages, including abstraction mechanisms, types, scoping, binding, control flow, subprograms, and concurrency.
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Compares imperative and declarative models using multiple programming languages. Examines implementation strategies, memory model, and programming environments. Includes lectures and problem sessions in mathematics, programming, problem solving, and CSS applications. Develops student understanding of how compliers translate high level programming languages into assembly language. Includes specifying programming language syntax, building data structures, generating assembly code, and implementing a complier for a small high-level language. 0 in both TCSS 342 and TCSS 371.
Examines the fundamental concepts of operating systems and how they function. Includes process management, file systems, concurrency, inter-process communication, graphical interfaces, and security. Covers cryptographic methods including public and private key algorithms. Examines protocols that utilize such methods, such as secure email, digital signatures, authorization, e-voting, and electronic cash. Includes lab component for demonstration of security techniques such as firewalls, intrusion detection systems, and virtual private networks.
Introduction to the uses of intelligence theories, techniques, and tools. Foundational material includes search, knowledge representation, machine learning, and planning. Artificial intelligence techniques applied to practical problems in areas such as control systems, optimization, scheduling, and classification. Explores algorithmic design options for motion control, navigation, and obstacle avoidance in mobile autonomous robots. Introduces pertinent principles from artificial intelligence and embedded real-time systems.
Students construct robots from kits and program them to demonstrate sophisticated behaviors. Covers languages, finite automata, regular expressions, context-free grammars, and other automata such as pushdown store machines and Turing machines. Includes models of computation, computable and non-computable functions, non-determinism, space and time complexity, tractable and intractable functions, non-determinism, space and time. Fundamental concepts, system organization, and implementation of database systems. Explores application life cycle, user interfaces, data management, graphics libraries, memory management, localization, and web services.
Examines human-centered design of interactive systems. Focuses on understanding user needs, brainstorming, sketching, choosing from among design alternatives, prototyping, usability testing, representing, communicating, and critiquing designs. 0 in either TCSS 305 or T INST 312. Introduces methods for supervised and unsupervised machine learning, such as decision trees, random forests, boosted decision trees, logistic regression, neural networks, deep learning, clustering, and association rule mining. Prerequisite: TCSS 343, or permission from instructor. Includes relevant background material in linguistics, mathematics, probability theory, and computer science.
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Covers text similarly, part of speech tagging, parsing, semantics, question answering, sentiment analysis, and text summarization. Introduction to the main concepts in image synthesis, modeling, and animation. Topics include displays, drawing and rendering algorithms, geometric transformations, 2- and 3D viewing, objects representation, and computer animation. Analyzes system re-engineering, domain-specific languages, generative development, system design and service-oriented architecture. Also covers how to handle legacy systems, utilize model driven software development to automate code generation and understand low to high level architectures, by using software engineering methodologies, refactoring, UML, and the Eclipse framework. An examination of particular theory and practice in designing software embedded in electronic devices and controllers. Includes clocks, threads, multitasking, critical sections, monitors, scheduling, on chip and external device interfacing, communications, and fault tolerance.
Introduces basic concepts and techniques used in the analysis of biological data, as well as applications of computational techniques in biological applications. Students will learn biology concepts and vocabulary. The programming language R primarily will be used. No background in biology is required. Study and comparison of several programming languages in regards to data structures, operations, notation, and control. Examines programming paradigms, implementation strategies, programming environments, and programming style.
0 in both TCSS 342 and TCSS 333. Discusses the theoretical and practical issues surrounding computer security and data protection. Prerequisite: either T INST 312, which may be taken concurrently, or a minimum grade of 2. 0 in both TCSS 342 and TCSS 325. Covers basic concepts of cryptography, including authentication, public key cryptography, and digital signatures. Additionally, it covers modern definitions of security, implementation aspects of cryptographic schemes and their use in computer networks and the internet.
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0 in TCSS 321 or TMATH 125 or TMATH 402. Covers electronic communication over noisy channels, and digital storage on various types of media. 0 in TMATH 308 or TCSS 321. Examines current topics and issues associated with computing and software systems. Development of large-scale software project in advanced imaging involving computational intelligence and artificial life applied to such fields as game development or virtual reality. Students work in interdisciplinary teams that integrate several computer science areas. Advanced topics: game programming, artificial life, virtual humans, and computer animation.
Project as delineated in a contract between student, faculty advisor, and community sponsor. Readings as specified in agreement with faculty. Design and implementation of a research study as specified in a contract with faculty. Examines foundational aspects of both enterprise and architectural thinking, including the application software to technology to solution architecture continuum, the role of EA in business and IT alignment, architectural styles and techniques for capturing and documenting architectures. Practices techniques for analyzing and reasoning about architectures. Examines advanced aspects of enterprise architecture practices and its application to guide and support business strategy. An EA maturity framework and governance practices are developed through a case study on applying evolving technologies – cloud, mobile, social media, big data in the EA context to solve business problems.
Provides an introduction to cloud computing and virtualization – enabling multiple instances of operating systems to be run on a single physical system. Covers computational models including finite automata, regular expressions, context-free grammars, pushdown automata, Turing machines, and techniques for analyzing them. Basic computability theory and undecidability, computational complexity theory, and NP-completeness. Prepares students for analysis and use of advanced algorithms. Examines math concepts on linear algebra and linear transformation, and subjects on singular value decomposition, Fourier transforms, Wavelet transforms, and other topics. Students apply these math concepts and implement numerical solutions to problems in areas including pattern recognition, information retrieval, web search, image processing, cryptography, and machine learning.
Examines a variety of techniques to perform data analytics and their extensions to big data infrastructure. Students will be able to identify mathematical foundations of data analytics, data analyses algorithms and tools. Introduces big data infrastructure, distributed computational paradigm, and distributed data analytics algorithms. 0 in TCSS 343 and TCSS 445 or equivalent. IR, and evaluation of IR systems. Explores learning and predictive modeling methods for data analysis, such as decision trees, instance based earning, Baysian learning, neural networks, ensemble methods, and support vector machines.
Surveys fundamental concepts of learning theory. Investigates a service-oriented computing paradigm for use with the Internet, web services. Includes comparisons of distributed computing paradigms, enterprise application integration, service oriented architecture, web services concepts, web services technologies, service coordination protocols, service compositions, and service applications. Analyzes the internals of a database system and the principles of building a database engine, including buffer management, query execution and optimization, and transaction management.
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Provides hands-on experience on the internals of one of the commercial database management systems as a case study. 0 in TCS 343 or equivalent. In addition to signals and systems, linear transforms and analysis, state machines and how to build models of hybrid systems, the course also introduces basics of embedded systems and the computation models of systems, including both software components and physical dynamics. Examines physical design and logical design of Internet of Things, functional blocks and architecture, protocols and communication models, enabling technologies, application domains specific to Internet of Things, smart objects, development tools, system management, cloud services, security and data analytics.
Prerequisite: TCSS 558, or permission from instructor. Recommended: PHYS 122 and PHYS 123 or Electromagnetics Basics. Reviews the current state of network control efforts for CPS. Studies recent development of control algorithms for CPS. Focuses on approaches to deliver fully distributed control over wireless sensor networks from control theoretic perspective. Prerequisite: TCSS 569, or permission from the instructor. Covers simple ciphers, block and stream ciphers, attacks, public-key ciphers, electronic signature, cryptographic algorithms, and real-world examples.
Covers advanced topics of cryptographic protocols, including formal definitions of security, composability, zero knowledge proofs, commitment schemes, oblivious transfer, secure two-party computations and secure multi-play computations. 0 in TCSS 540, TCSS 543 or TCSS 581. Covers fundamentals of Shor’s attack against conventional cryptography and notions of quantum-resistant cryptosystems. Includes the main lattice-based schemes for encryption, signatures, and homomorphic encryption, as well as code-based encryption, hash-based, and multivariate digital signatures. Additionally, highlights research problems and deployment issues of the technique. Covers applications of computational techniques in various biological applications, including sequence analysis, systems biology, personalized medicine, and drug discovery. Focuses on machine learning methods in mining big data sources in biology.
Examines current graduate-level topics and issues associated with computing and software systems. Discusses recent developments in bioinformatics, focusing on machine learning methods and integration of big biology data. Consists of reading papers, surveying the latest methods, and tools developed for high dimensional data. Discusses recent developments in data science, focusing on applications and advances in data management and mining for data from a variety of domains. Consists of reading papers, surveying the latest tools, and techniques of data science.