Privacy Preserving Data Mining

Ljiljana Brankovic, Md Zahidul Islam, Helen Giggins

Research output: Book chapter/Published conference paperChapter

Abstract

Despite enormous benefits and the extremely fast proliferation of data mining in recent years, data owners and researchers alike have acknowledged that data mining also revives old and brings in new threats to individual privacy.Many believe that data mining is, and will continue to be, one of the most significant privacy challenges in years to come.We live in an information age where vast amounts of personal data are regularly being collected in the process of bank transactions, credit card payments,making phone calls, using reward cards, visiting doctors and renting videos and cars, to mention but a few. All these data are typically used for data mining and statistical analysis and often sold to other companies and organizations.A breach of privacy occurs when individuals are not aware that the data have been collected in the first place, have been passed onto the other companies and organizations, or have been used for purposes other than the one for which they were originally collected.Even when individuals approve of using their personal records for datamining and statistical analysis, for example for medical research, it is still assumed that only aggregate values will be made available to researchers and that no individual values will be disclosed. There are several possible techniques that can be employed in order to ensure this privacy requirement. They include adding noise to the original data, so that disclosing a perturbed individual value does not necessarily imply a breach of privacy. Some techniques were developed specifically for mining vertically and/or horizontally partitioned data. Each partition belongs to a different party (e.g., a hospital), and no party is willing to share their data with others but they all have interest in mining the total data set comprising all of the partitions.Other techniques are suitable for scenarios where logic rules and patterns discovered from data can themselves present a threat to privacy. In this chapter we introduce the problem, provide a classification of existing techniques and survey the most important results in this area.
Original languageEnglish
Title of host publicationSecurity, Privacy and Trust in Modern Data Management
EditorsMilan Petkovic, Willem Jonker Willem Jonker
Place of PublicationThe Netherlands
PublisherSpringer
Pages151-166
Number of pages16
Edition11
ISBN (Print)9783540698609
Publication statusPublished - 2007

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  • Cite this

    Brankovic, L., Islam, M. Z., & Giggins, H. (2007). Privacy Preserving Data Mining. In M. Petkovic, & W. J. W. Jonker (Eds.), Security, Privacy and Trust in Modern Data Management (11 ed., pp. 151-166). Springer.