PPT-Making verifiable computation a systems problem

Author : giovanna-bartolotta | Published Date : 2015-09-28

Michael Walfish The University of Texas at Austin From a systems perspective it is an exciting time for this area When we started there were no implementations

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Making verifiable computation a systems problem: Transcript


Michael Walfish The University of Texas at Austin From a systems perspective it is an exciting time for this area When we started there were no implementations my colleagues thought I was a lunatic. Srinath. . Setty. , Richard McPherson,. Andrew J. Blumberg, and Michael Walfish. The University of Texas at Austin. The motivation is 3. rd. party computing: cloud, volunteers, etc.. We desire the following properties in the above exchange:. © John Wiley & Sons Canada, Ltd.. Business Leadership: Management Fundamentals. John R. . Schermerhorn. , Jr. ., . Barry Wright, and Lorie Guest. Data. Raw facts and observations. Information. Data made useful for decision-making. Michael Walfish. The University of Texas at Austin. From a systems perspective, it is an exciting time for this area!. When we started …. … there were no implementations. … my colleagues thought I was a lunatic. © John Wiley & Sons Canada, Ltd.. Business Leadership: Management Fundamentals. John R. . Schermerhorn. , Jr. ., . Barry Wright, and Lorie Guest. Understand the role of . information in . the management process. Towards Practical Verifiable Graph Processing. Yupeng. Zhang, . Charalampos. . Papamanthou. and . Jonathan . Katz. University of . Maryland. Agenda. Background of verifiable computation and motivation. in this presentation were developed by Rowan professor Mark Hale. Professor Hale is a Cognitive . Psychologist and . Human . Factors . Specialist and very gratefully contributed his material to this course in . In The Large. David Millard. dem@soton.ac.uk. | @. hoosfoos. | . davidmillard.org. Problem Solving in the Large. Problem Solving in the Large. Real World. Application Domain. Computational. Domain. Fall 2017. http://cseweb.ucsd.edu/. classes/fa17/cse105-a/. Today's learning goals . Sipser Ch 5.1. Define and explain core examples of decision problems: A. DFA. , E. DFA. , EQ. DFA. , A. TM. , . HALT. Yupeng. Zhang. , Daniel . Genkin. , Jonathan Katz, . Dimitrios. Papadopoulos and . Charalampos. . Papamanthou. . Cloud Computing. Individuals . Companies. Universities. Security Issue: Integrity. : active efforts to discover what must be done to achieve a goal that is not readily attainable. TYPES OF PROBLEMS. Problems of inducing structure. : relations among numbers, words, symbols, ideas. Problems of arrangement. Fall . 2017. http://cseweb.ucsd.edu/classes/fa17/cse105-a/. Learning goals. Introductions. Clickers. When did you take CSE 20?. Winter 2017. Fall 2016. Spring 2016. Winter 2016. PETER 108: AC. To change your remote frequency. Victor Vu, . Srinath. . Setty. ,. Andrew J. Blumberg, and Michael Walfish. The University of Texas at Austin. This should be:. 1. . Unconditional. , meaning no assumptions about the server. 2. . General-purpose. MAKING. . . . INTRODUCTION. Decision making is a key managerial responsibility. Decision making is a choice amongst alternatives. Steps involved:. Defining the problem. Setting the objectives. Collecting data. J. Alex Halderman Brent Waters. . Princeton University SRI International. Complete audit expensive . . seek . probabilistic . guarantee. Who chooses what to audit?. Motivating Example.

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