Cost Comparison Among Provable Data Possession Schemes

Abstract

Provable data possession (PDP) provides mechanisms to efficiently audit the integrity of data held by third parties, like cloud service providers. While multiple PDP schemes have been proposed, there is no research to date that provides in-depth cost analysis for PDP. This research fills that gap by (1) collecting and analyzing cost data for four PDP schemes, (2) providing generic cost models (mathematical formulae expressing abstract models which can be used to infer future cost), and (3) comparing overall cost efficiency of each PDP scheme. For the schemes considered in this study, we find all have nearly identical costs in practice; however, sophisticated schemes designed with low communication complexity have higher preprocessing or storage costs which, depending on audit parameters, impact total scheme cost. We conclude that MAC-PDP and CPOR schemes are similar, whereas the cost of A-PDP becomes relatively expensive at large file sizes. Our basis cost projections show tagging, storing and auditing a file for one year at one audit per hour is at least $160 for a 1 GB file, $170 for a 1 TB file, and $2,000 for a 1 PB file using a cost model based on the Amazon S3service.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2016
Accession Number
AD1027181

Entities

People

  • Stephen J. Bremer

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Auditing
  • Cloud Computing
  • Cloud Storage
  • Computer Network Security
  • Computers
  • Cost Analysis
  • Cost Models
  • Cryptography
  • Cybersecurity
  • Data Storage Systems
  • Department Of Defense
  • Efficiency
  • New York
  • Notation
  • United States

Fields of Study

  • Computer science

Readers

  • Defense Financial Management and Audit.
  • Distributed Systems and Data Platform Development
  • Parallel and Distributed Computing.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms