ECE 407

Cryptography is a powerful toolbox for building secure systems --- not just for private communication, but also for building fault tolerant protocols, for securely outsourcing computation to untrusted services, and more. The goal of this course is to introduce the concepts of modern cryptography, including a combination of theoretical foundations (how do we precisely state security guarantees and assumptions, and prove that a protocol is designed correctly?) and practical techniques (how do we combine secure primitives to make effective systems?). This course is intended for senior undergraduate students with an interest in applying cryptographic techniques to building secure systems, and for graduate students with an interest in cryptography or systems security. Course Information: Same as CS 407. 3 or 4 undergraduate hours. 3 or 4 graduate hours. Prerequisite: CS 225.

• Introduction: nature of engineering decisions; structuring of decisions; role of models; interplay of economics and technical/engineering considerations; decision making under certainty and uncertainy; good decisions vs. good outcomes; tools
• Resource allocation decision making using the linear programming framework: problem formulation; basic approach; duality; economic interpretation; sensitivity analysis; interpretation of results
• Scheduling and assignment decisions using network flow concepts: transshipment problem formulation and solution; application to matching decisions; network optimization; scheduling applications
• Sequential decision making in a dynamic programming framework: nature of dynamic programming approach; problem formulation; solution procedures; key limitations
• Probability theory: random variables; probability distributions; expectation; conditional probability; moments; convolution
• Statistical concepts: data analysis; statistical measures; estimation
• Application of probabilistic concepts to the modeling of uncertainty in decision making: modeling of the impacts of uncertainty; applications to siting, investment and price volatility problems
• Decision making under uncertainty: decision trees; value of information; uses of data; sensitivity analysis and statistics
• Case studies and presentations

Topics:

• Introduction: nature of engineering decisions; structuring of decisions; role of models; interplay of economics and technical/engineering considerations; decision making under certainty and uncertainy; good decisions vs. good outcomes; tools
• Resource allocation decision making using the linear programming framework: problem formulation; basic approach; duality; economic interpretation; sensitivity analysis; interpretation of results
• Scheduling and assignment decisions using network flow concepts: transshipment problem formulation and solution; application to matching decisions; network optimization; scheduling applications
• Sequential decision making in a dynamic programming framework: nature of dynamic programming approach; problem formulation; solution procedures; key limitations
• Probability theory: random variables; probability distributions; expectation; conditional probability; moments; convolution
• Statistical concepts: data analysis; statistical measures; estimation
• Application of probabilistic concepts to the modeling of uncertainty in decision making: modeling of the impacts of uncertainty; applications to siting, investment and price volatility problems
• Decision making under uncertainty: decision trees; value of information; uses of data; sensitivity analysis and statistics
• Case studies and presentations

A. Ravindran, D. T. Philips, and J. J. Solberg, Operations Research: Principles and Practice. New York: J. Wiley, 1992.

2/13/2013