An audit environment for outsourcing of frequent itemset mining

  • Authors:

  • W. K. Wong;David W. Cheung;Edward Hung;Ben Kao;Nikos Mamoulis

  • Affiliations:

  • The University of Hong Kong;The University of Hong Kong;The Hong Kong Polytechnic University;The University of Hong Kong;The University of Hong Kong

  • Venue:
  • Proceedings of the VLDB Endowment
  • Year:
  • 2009

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Abstract

Finding frequent itemsets is the most costly task in association rule mining. Outsourcing this task to a service provider brings several benefits to the data owner such as cost relief and a less commitment to storage and computational resources. Mining results, however, can be corrupted if the service provider (i) is honest but makes mistakes in the mining process, or (ii) is lazy and reduces costly computation, returning incomplete results, or (iii) is malicious and contaminates the mining results. We address the integrity issue in the outsourcing process, i.e., how the data owner verifies the correctness of the mining results. For this purpose, we propose and develop an audit environment, which consists of a database transformation method and a result verification method. The main component of our audit environment is an artificial itemset planting (AIP) technique. We provide a theoretical foundation on our technique by proving its appropriateness and showing probabilistic guarantees about the correctness of the verification process. Through analytical and experimental studies, we show that our technique is both effective and efficient.