Composite Bloom Filters for Secure Record Linkage
Identifieur interne : 000146 ( Pmc/Curation ); précédent : 000145; suivant : 000147Composite Bloom Filters for Secure Record Linkage
Auteurs : Elizabeth Ashley Durham ; Murat Kantarcioglu ; Yuan Xue ; Csaba Toth ; Mehmet Kuzu ; Bradley MalinSource :
- IEEE transactions on knowledge and data engineering [ 1041-4347 ] ; 2014.
Abstract
The process of record linkage seeks to integrate instances that
correspond to the same entity. Record linkage has traditionally been performed
through the comparison of identifying field values (
Url:
DOI: 10.1109/TKDE.2013.91
PubMed: 25530689
PubMed Central: 4269299
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Composite Bloom Filters for Secure Record Linkage</title>
<author><name sortKey="Durham, Elizabeth Ashley" sort="Durham, Elizabeth Ashley" uniqKey="Durham E" first="Elizabeth Ashley" last="Durham">Elizabeth Ashley Durham</name>
</author>
<author><name sortKey="Kantarcioglu, Murat" sort="Kantarcioglu, Murat" uniqKey="Kantarcioglu M" first="Murat" last="Kantarcioglu">Murat Kantarcioglu</name>
</author>
<author><name sortKey="Xue, Yuan" sort="Xue, Yuan" uniqKey="Xue Y" first="Yuan" last="Xue">Yuan Xue</name>
</author>
<author><name sortKey="Toth, Csaba" sort="Toth, Csaba" uniqKey="Toth C" first="Csaba" last="Toth">Csaba Toth</name>
</author>
<author><name sortKey="Kuzu, Mehmet" sort="Kuzu, Mehmet" uniqKey="Kuzu M" first="Mehmet" last="Kuzu">Mehmet Kuzu</name>
</author>
<author><name sortKey="Malin, Bradley" sort="Malin, Bradley" uniqKey="Malin B" first="Bradley" last="Malin">Bradley Malin</name>
</author>
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<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Composite Bloom Filters for Secure Record Linkage</title>
<author><name sortKey="Durham, Elizabeth Ashley" sort="Durham, Elizabeth Ashley" uniqKey="Durham E" first="Elizabeth Ashley" last="Durham">Elizabeth Ashley Durham</name>
</author>
<author><name sortKey="Kantarcioglu, Murat" sort="Kantarcioglu, Murat" uniqKey="Kantarcioglu M" first="Murat" last="Kantarcioglu">Murat Kantarcioglu</name>
</author>
<author><name sortKey="Xue, Yuan" sort="Xue, Yuan" uniqKey="Xue Y" first="Yuan" last="Xue">Yuan Xue</name>
</author>
<author><name sortKey="Toth, Csaba" sort="Toth, Csaba" uniqKey="Toth C" first="Csaba" last="Toth">Csaba Toth</name>
</author>
<author><name sortKey="Kuzu, Mehmet" sort="Kuzu, Mehmet" uniqKey="Kuzu M" first="Mehmet" last="Kuzu">Mehmet Kuzu</name>
</author>
<author><name sortKey="Malin, Bradley" sort="Malin, Bradley" uniqKey="Malin B" first="Bradley" last="Malin">Bradley Malin</name>
</author>
</analytic>
<series><title level="j">IEEE transactions on knowledge and data engineering</title>
<idno type="ISSN">1041-4347</idno>
<idno type="eISSN">1558-2191</idno>
<imprint><date when="2014">2014</date>
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<front><div type="abstract" xml:lang="en"><p id="P1">The process of record linkage seeks to integrate instances that
correspond to the same entity. Record linkage has traditionally been performed
through the comparison of identifying field values (<italic>e.g.,
Surname</italic>
), however, when databases are maintained by disparate
organizations, the disclosure of such information can breach the privacy of the
corresponding individuals. Various private record linkage (PRL) methods have
been developed to obscure such identifiers, but they vary widely in their
ability to balance competing goals of accuracy, efficiency and security. The
tokenization and hashing of field values into Bloom filters (BF) enables greater
linkage accuracy and efficiency than other PRL methods, but the encodings may be
compromised through frequency-based cryptanalysis. Our objective is to adapt a
BF encoding technique to mitigate such attacks with minimal sacrifices in
accuracy and efficiency. To accomplish these goals, we introduce a
statistically-informed method to generate BF encodings that integrate bits from
multiple fields, the frequencies of which are provably associated with a minimum
number of fields. Our method enables a user-specified tradeoff between security
and accuracy. We compare our encoding method with other techniques using a
public dataset of voter registration records and demonstrate that the increases
in security come with only minor losses to accuracy.</p>
</div>
</front>
</TEI>
<pmc article-type="research-article"><pmc-comment>The publisher of this article does not allow downloading of the full text in XML form.</pmc-comment>
<pmc-dir>properties manuscript</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-journal-id">9887654</journal-id>
<journal-id journal-id-type="pubmed-jr-id">36377</journal-id>
<journal-id journal-id-type="nlm-ta">IEEE Trans Knowl Data Eng</journal-id>
<journal-id journal-id-type="iso-abbrev">IEEE Trans Knowl Data Eng</journal-id>
<journal-title-group><journal-title>IEEE transactions on knowledge and data engineering</journal-title>
</journal-title-group>
<issn pub-type="ppub">1041-4347</issn>
<issn pub-type="epub">1558-2191</issn>
</journal-meta>
<article-meta><article-id pub-id-type="pmid">25530689</article-id>
<article-id pub-id-type="pmc">4269299</article-id>
<article-id pub-id-type="doi">10.1109/TKDE.2013.91</article-id>
<article-id pub-id-type="manuscript">NIHMS506202</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Article</subject>
</subj-group>
</article-categories>
<title-group><article-title>Composite Bloom Filters for Secure Record Linkage</article-title>
</title-group>
<contrib-group><contrib contrib-type="author"><name><surname>Durham</surname>
<given-names>Elizabeth Ashley</given-names>
</name>
<aff id="A1">Dept. of Biomedical Informatics, Vanderbilt University, Nashville, TN 37232.<email>ea.durham@vanderbilt.edu</email>
</aff>
</contrib>
<contrib contrib-type="author"><name><surname>Kantarcioglu</surname>
<given-names>Murat</given-names>
</name>
<role>Senior Member, IEEE</role>
<aff id="A2">Department of Computer Science, University of Texas at Dallas, Richardson, TX, 75083.<email>muratk@utdallas.edu</email>
</aff>
</contrib>
<contrib contrib-type="author"><name><surname>Xue</surname>
<given-names>Yuan</given-names>
</name>
<role>Member, IEEE</role>
<aff id="A3">Dept. of Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN 37232.<email>yuan.xue@vanderbilt.edu</email>
</aff>
</contrib>
<contrib contrib-type="author"><name><surname>Toth</surname>
<given-names>Csaba</given-names>
</name>
<aff id="A4">Dept. of Biomedical Informatics, Vanderbilt University, Nashville, TN 37232.<email>csaba.toth@vanderbilt.edu</email>
</aff>
</contrib>
<contrib contrib-type="author"><name><surname>Kuzu</surname>
<given-names>Mehmet</given-names>
</name>
<aff id="A5">Dept. of Computer Science, University of Texas at Dallas, Richardson, TX, 75083.<email>mehmet.kuzu@utdallas.edu</email>
</aff>
</contrib>
<contrib contrib-type="author"><name><surname>Malin</surname>
<given-names>Bradley</given-names>
</name>
<role>Member, IEEE</role>
<aff id="A6">Depts. of Biomedical Informatics and EECS, Vander bilt University, Nashville, TN 37232.<email>b.malin@vanderbilt.edu</email>
</aff>
</contrib>
</contrib-group>
<pub-date pub-type="nihms-submitted"><day>5</day>
<month>9</month>
<year>2014</year>
</pub-date>
<pub-date pub-type="ppub"><month>12</month>
<year>2014</year>
</pub-date>
<pub-date pub-type="pmc-release"><day>01</day>
<month>12</month>
<year>2015</year>
</pub-date>
<volume>26</volume>
<issue>12</issue>
<fpage>2956</fpage>
<lpage>2968</lpage>
<pmc-comment>elocation-id from pubmed: 10.1109/TKDE.2013.91</pmc-comment>
<permissions><copyright-statement>© 2013 IEEE</copyright-statement>
<copyright-year>2013</copyright-year>
</permissions>
<abstract><p id="P1">The process of record linkage seeks to integrate instances that
correspond to the same entity. Record linkage has traditionally been performed
through the comparison of identifying field values (<italic>e.g.,
Surname</italic>
), however, when databases are maintained by disparate
organizations, the disclosure of such information can breach the privacy of the
corresponding individuals. Various private record linkage (PRL) methods have
been developed to obscure such identifiers, but they vary widely in their
ability to balance competing goals of accuracy, efficiency and security. The
tokenization and hashing of field values into Bloom filters (BF) enables greater
linkage accuracy and efficiency than other PRL methods, but the encodings may be
compromised through frequency-based cryptanalysis. Our objective is to adapt a
BF encoding technique to mitigate such attacks with minimal sacrifices in
accuracy and efficiency. To accomplish these goals, we introduce a
statistically-informed method to generate BF encodings that integrate bits from
multiple fields, the frequencies of which are provably associated with a minimum
number of fields. Our method enables a user-specified tradeoff between security
and accuracy. We compare our encoding method with other techniques using a
public dataset of voter registration records and demonstrate that the increases
in security come with only minor losses to accuracy.</p>
</abstract>
<kwd-group><kwd>data matching</kwd>
<kwd>record linkage</kwd>
<kwd>entity resolution</kwd>
<kwd>privacy</kwd>
<kwd>security</kwd>
<kwd>Bloom filter</kwd>
</kwd-group>
</article-meta>
</front>
</pmc>
</record>
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