PPT-On the Cryptographic Complexity of the Worst Functions

Amos Beimel BGU Yuval Ishai Technion Ranjit Kumaresan Technion Eyal Kushilevitz Technion How Bad are the Worst Functions Function class F N of all functions . Cryptographic systems serving the US government and military spanning a range from nuclear command and control to tactical radios for the battlefield to network security devices use my algorithms For the last 14 years I have been a Technical Directo The package provides tools to handle Boolean functions in particular for cryp tographic purposes This document guides the user through some code examples and gives a feel of what can be done with the package A Boolean function is a mapping 01 8594 0 Easter Carolyn French Information Technology Laboratory National Institute of Standards and Technology Gaithersburg MD 20899 8930 US Department of Commerce Penny Pritzker Secretary National Institute of Standards and Technology Willie E May acting Shantanu. . Dutt. ECE Dept.. UIC. Time Complexity. An algorithm time complexity is a function T(n) of problem size n that represents how much time the algorithm will take to complete its task.. Note that there could be more than one problem size parameter n, in which case we can denote the time complexity function as T(S), where S is the set of size parameters. E.g., for the shortest path problem on a graph G, we have 2 size parameters, n the # of vertices and e the # of edges (thus T(S) = T(. Shantanu. . Dutt. ECE Dept.. UIC. Time Complexity. An . algorithm’s . time complexity is a function T(n) of problem size n that represents how much time the algorithm will take to complete its task.. Auxiliary Input or a Universal Simulator. Nir. . Bitansky. . Ran Canetti. Henry Cohn. Shafi. . Goldwasser. Yael . Tauman-Kalai. . Omer Paneth. Alon. Rosen. Program Obfuscation. Obfuscated program. Uninformed search . algorithms. Discussion Class CS 171. Friday, October, 2nd. (Please read lecture topic material before and after each lecture on that topic). Thanks to professor . Kask. Some of the slides (page 2-7) were copied from his lectures.. Prof. Andy Mirzaian. Machine Model. &. Time Complexity. STUDY MATERIAL:. . [CLRS]. chapters 1, 2, 3. Lecture Note. 2. 2. Example. Time Complexity. Execution time. n. 1 sec.. n log n. Applied Discrete Mathematics Week 2: Functions and Sequences. 1. … and now for…. Sequences. February 2, 2017. Applied Discrete Mathematics Week 2: Functions and Sequences. Leftover Asymptotic . Analysis. Spring 2016. Richard Anderson. Lecture . 4a. 2. Announcements. Homework #1 due Wednesday, April 6. 3. Definition of Order Notation. h(n) . є. O(f(n)) . Big-O “Order”. Presented by John . Shu. Shouhuai. . Xu. and Keith . H. arrison. UTSA, Dept. Computer Science. Outline. Introduction. Threat Assessment . Understanding the Attack. Countering Memory Disclosure Attacks. Spring 2016. Richard Anderson. Lecture . 2. 2. Announcements. Homework requires you get the textbook (Either 2. nd. or 3. rd. Edition). Section . Thursday. Homework #1 out today (Wednesday). Due at the . dave@create.aau.dk. Source. Chapter 3 of. Cormen. , T. H., . Leiserson. , C. E., . Rivest. , R. L. and Stein, C. (2001). . Introduction to Algorithms. (Second Edition). MIT Press, Cambridge, MA.. Introduction. What is the best way to measure the time complexity of an algorithm?. - Best-case run time?. - Worst-case run time?. - Average run time?. Which should we try to optimize?. Best-Case Measures. How can we modify almost any algorithm to have a good best-case running time?.