Result integrity verification of outsourced frequent itemset mining

Boxiang Dong, Ruilin Liu, Hui Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

The data-mining-as-a-service (DMaS) paradigm enables the data owner (client) that lacks expertise or computational resources to outsource its mining tasks to a third-party service provider (server). Outsourcing, however, raises a serious security issue: how can the client of weak computational power verify that the server returned correct mining result? In this paper, we focus on the problem of frequent itemset mining, and propose efficient and practical probabilistic verification approaches to check whether the server has returned correct and complete frequent itemsets.

Original languageEnglish
Title of host publicationData and Applications Security and Privacy XXVII - 27th Annual IFIP WG 11.3 Conference, DBSec 2013, Proceedings
Pages258-265
Number of pages8
DOIs
StatePublished - 12 Aug 2013
Event27th Annual IFIP WG 11.3 Conference on Data and Applications Security and Privacy, DBSec 2013 - Newark, NJ, United States
Duration: 15 Jul 201317 Jul 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7964 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other27th Annual IFIP WG 11.3 Conference on Data and Applications Security and Privacy, DBSec 2013
CountryUnited States
CityNewark, NJ
Period15/07/1317/07/13

Keywords

  • Cloud computing
  • data mining as a service
  • integrity verification

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    Dong, B., Liu, R., & Wang, H. (2013). Result integrity verification of outsourced frequent itemset mining. In Data and Applications Security and Privacy XXVII - 27th Annual IFIP WG 11.3 Conference, DBSec 2013, Proceedings (pp. 258-265). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7964 LNCS). https://doi.org/10.1007/978-3-642-39256-6_17