Toward Practical Verification of Outsourced Computations Using Probabilistically Checkable Proofs (PCPs)

Abstract

This paper describes preliminary results and outlines the plan of attack for an on-going project to develop a practical system for verifying outsourced computations. We want a client to be able to describe a computation to a server, get back a purported output and some auxiliary information, and use that auxiliary information to verify that the output is correct. For outsourcing to be worthwhile, the verification process should be substantially more efficient than simply executing the computation. Our approach to this problem is to exploit the theory of probabilistically checkable proofs (PCPs). Specifically, our project seeks to build a bridge between the theory and an implementable system. We describe a protocol for outsourced computation that includes algorithmic refinements of the PCP protocol and end-to-end instantiation of the necessary steps (e.g., compilation to a form suitable for application of the PCP theorem). Although we are in the process of implementing the protocol and do not have experimental results, we present a detailed analysis that provides cause for optimism: our cycle and memory usage costs strongly suggest that our system will be useful. We focus on the example of matrix multiplication, where we show that for large matrices, our method achieves enormous savings for the client and requires feasible amounts of bandwidth.

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Document Details

Document Type
Technical Report
Publication Date
Jul 12, 2010
Accession Number
AD1024615

Entities

People

  • Andrew Blumberg
  • Michael Walfish
  • Srinath Setty

Organizations

  • University of Texas at Austin

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Cloud Storage
  • Coding
  • Collisions
  • Computations
  • Computer Programming
  • Computers
  • Construction
  • Data Mining
  • High Level Languages
  • Language
  • Linear Algebra
  • Notation
  • Numbers
  • Outsourcing
  • Polynomials
  • Programming Languages

Fields of Study

  • Computer science
  • Mathematics

Readers

  • Computer Networking
  • Neural Network Machine Learning.
  • Systems Analysis and Design