PPT-Probabilistic Programming for Security

Michael Hicks Piotr Peter Mardziel University of Maryland College Park Stephen Magill Galois Michael Hicks UMD Mudhakar Srivatsa IBM TJ Watson Jonathan Katz UMD Mário Alvim UFMG. Gordon Microsoft Research adgmicrosoftcom Thomas A Henzinger IST Austria tahistacat Aditya V Nori Microsoft Research adityanmicrosoftcom Sriram K Rajamani Microsoft Research srirammicrosoftcom Abstract Probabilistic programs are usual fu (goal-oriented). Action. Probabilistic. Outcome. Time 1. Time 2. Goal State. 1. Action. State. Maximize Goal Achievement. Dead End. A1. A2. I. A1. A2. A1. A2. A1. A2. A1. A2. Left Outcomes are more likely. 1. The Software Security Problem . Chih. Hung Wang. Reference:. 1. B. Chess and J. West, Secure Programming with Static Analysis, Addison-Wesley, 2007.. 2. R. C. . Seacord. , Secure Coding in C and C++, Addison-Wesley, 2006.. Dynamic Programming. 11.1 A Prototype Example for Dynamic Programming. The stagecoach problem. Mythical fortune-seeker . travels . West by stagecoach to join the gold rush in the mid-1900s. The origin . How the Quest for the Ultimate Learning Machine Will Remake Our World. Pedro Domingos. University of Washington. Machine Learning. Traditional Programming. Machine Learning. Computer. Data. Algorithm. Piotr. (Peter) . Mardziel. (UMD) . Kasturi. . Raghavan. (UCLA). Convenience. 2. ~. params. prior. ~. params. | [ model(~. params. ) == sample ]. posterior. B. 1. ~secret. Bob’s belief about secret. Learning Revealed. . Pedro Domingos. . University of Washington. Where Does Knowledge Come From?. Evolution. Experience. Culture. Where Does Knowledge Come From?. Evolution. Experience. Culture. Computers. Indranil Gupta. Associate Professor. Dept. of Computer Science, University of Illinois at Urbana-Champaign. Joint work with . Muntasir. . Raihan. . Rahman. , Lewis Tseng, Son Nguyen, . Nitin. . Vaidya. Dynamic Programming. Dynamic programming is a useful mathematical technique for making a sequence of interrelated decisions. It provides a systematic procedure for determining the optimal combination of decisions.. Chapter 3: Probabilistic Query Answering (1). 2. Objectives. In this chapter, you will:. Learn the challenge of probabilistic query answering on uncertain data. Become familiar with the . framework for probabilistic . Chapter 3: Probabilistic Query Answering (1). 2. Objectives. In this chapter, you will:. Learn the challenge of probabilistic query answering on uncertain data. Become familiar with the . framework for probabilistic . Chapter 5: Probabilistic Query Answering (3). 2. Objectives. In this chapter, you will:. Learn the definition and query processing techniques of a probabilistic query type. Probabilistic Reverse Nearest Neighbor Query. Chapter 7: Probabilistic Query Answering (5). 2. Objectives. In this chapter, you will:. Explore the definitions of more probabilistic query types. Probabilistic skyline query. Probabilistic reverse skyline query. Nathan Clement. Computational Sciences Laboratory. Brigham Young University. Provo, Utah, USA. Next-Generation Sequencing. Problem Statement . Map next-generation sequence reads with variable nucleotide confidence to .