AUDIO: An integrity auditing framework of outlier-mining-as-a-service systems

Ruilin Liu, Hui Wang, Anna Monreale, Dino Pedreschi, Fosca Giannotti, Wenge Guo

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

12 Scopus citations

Abstract

Spurred by developments such as cloud computing, there has been considerable recent interest in the data-mining-as-a-service paradigm. Users lacking in expertise or computational resources can outsource their data and mining needs to a third-party service provider (server). Outsourcing, however, raises issues about result integrity: how can the data owner verify that the mining results returned by the server are correct? In this paper, we present AUDIO, an integrity auditing framework for the specific task of distance-based outlier mining outsourcing. It provides efficient and practical verification approaches to check both completeness and correctness of the mining results. The key idea of our approach is to insert a small amount of artificial tuples into the outsourced data; the artificial tuples will produce artificial outliers and non-outliers that do not exist in the original dataset. The server's answer is verified by analyzing the presence of artificial outliers/non-outliers, obtaining a probabilistic guarantee of correctness and completeness of the mining result. Our empirical results show the effectiveness and efficiency of our method.

Original languageEnglish (US)
Title of host publicationMachine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2012, Proceedings
Pages1-18
Number of pages18
EditionPART 2
DOIs
StatePublished - 2012
Event2012 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2012 - Bristol, United Kingdom
Duration: Sep 24 2012Sep 28 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume7524 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other2012 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2012
CountryUnited Kingdom
CityBristol
Period9/24/129/28/12

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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