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SUMMARY:Advanced Techniques for Privacy-Preserving Linking of Multiple Lar
 ge Databases - Dinusha Vatsalan (Australian National University)
DTSTART:20160913T133000Z
DTEND:20160913T140000Z
UID:TALK67332@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:<span>Co-author: Peter Christen (The Australian National  Univ
 ersity) <br></span> <br>In the era of Big Data the collection of person-sp
 ecific data disseminated in  diverse databases provides enormous opportuni
 ties for businesses and governments  by exploiting data linked across thes
 e databases. Linked data empowers quality  analysis and decision making th
 at is not possible on individual databases.  Therefore\, linking databases
  is increasingly being required in many application  areas\, including hea
 lthcare\, government services\, crime and fraud detection\,  national secu
 rity\, and business applications. Linking data from different  databases r
 equires comparison of quasi-identifiers (QIDs)\, such as names and  addres
 ses. These QIDs are personal identifying attributes that contain sensitive
   and confidential information about the entities represented in these dat
 abases.  The exchange or sharing of QIDs across organisations for linkage 
 is often  prohibited due to laws and business policies. Privacy-preserving
  record linkage  (PPRL) has been an active research area over the past two
  decades addressing  this problem through the development of techniques th
 at facilitate the linkage  on masked (encoded) records such that no privat
 e or confidential information  needs to be revealed. <br> <span><br>Most o
 f the work in PPRL thus far has concentrated on linking two databases  onl
 y. Linking multiple databases has only recently received more attention as
  it  is being required in a variety of application areas. We have develope
 d several  advanced techniques for practical PPRL of multiple large databa
 ses addressing  the scalability\, linkage quality\, and privacy challenges
 . Our approaches perform  linkage on masked records using Bloom filter enc
 oding\, which is a widely used  masking technique for PPRL. In this talk\,
  we will first highlight the challenges  of PPRL of multiple databases\, t
 hen describe our developed approaches\, and then  discuss future research 
 directions required to leverage the huge potential that  linked data from 
 multiple databases can provide for businesses and government  services.</s
 pan>
LOCATION:Seminar Room 1\, Newton Institute
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