dc.contributor.author | Amirbekyan, Artak | |
dc.contributor.author | Estivill-Castro, Vladimir | |
dc.date.accessioned | 2017-05-03T14:16:00Z | |
dc.date.available | 2017-05-03T14:16:00Z | |
dc.date.issued | 2009 | |
dc.date.modified | 2010-08-05T07:15:38Z | |
dc.identifier.issn | 0219-1377 | |
dc.identifier.doi | 10.1007/s10115-009-0233-z | |
dc.identifier.uri | http://hdl.handle.net/10072/30120 | |
dc.description.abstract | Finding the nearest k objects to a query object is a fundamental operation for many data mining algorithms. With the recent interest in privacy, it is not surprising that there is strong interest in k-NN queries to enable clustering, classification and outlier-detection tasks. However, previous approaches to privacy-preserving k-NN have been costly and can only be realistically applied to small data sets. In this paper, we provide efficient solutions for k-NN queries for vertically partitioned data. We provide the first solution for the L(inf) (or Chessboard) metric as well as detailed privacy-preserving computation of all other Minkowski metrics. We enable privacy-preserving L(inf) by providing a practical approach to the Yao's millionaire problem with more than two parties. This is based on a pragmatic and implementable solution to Yao's millionaire problem with shares. We also provide privacy-preserving algorithms for combinations of local metrics into a global metric that handles the large dimensionality and diversity of attributes common in vertically partitioned data. To manage very large data sets, we provide a privacy-preserving SASH (a very successful data structure for associative queries in high dimensions). Besides providing a theoretical analysis, we illustrate the efficiency of our approach with an empirical evaluation. | |
dc.description.peerreviewed | Yes | |
dc.description.publicationstatus | Yes | |
dc.language | English | |
dc.language.iso | eng | |
dc.publisher | Springer | |
dc.publisher.place | United Kingdom | |
dc.relation.ispartofstudentpublication | N | |
dc.relation.ispartofpagefrom | 327 | |
dc.relation.ispartofpageto | 363 | |
dc.relation.ispartofissue | 3 | |
dc.relation.ispartofjournal | Knowledge and Information Systems | |
dc.relation.ispartofvolume | 21 | |
dc.rights.retention | Y | |
dc.subject.fieldofresearch | Distributed computing and systems software not elsewhere classified | |
dc.subject.fieldofresearch | Information systems | |
dc.subject.fieldofresearchcode | 460699 | |
dc.subject.fieldofresearchcode | 4609 | |
dc.title | Practical protocol for Yao’s millionaires problem enables secure multi-party computation of metrics and efficient privacy-preserving k-NN for large data sets | |
dc.type | Journal article | |
dc.type.description | C1 - Articles | |
dc.type.code | C - Journal Articles | |
gro.faculty | Griffith Sciences, School of Information and Communication Technology | |
gro.date.issued | 2009 | |
gro.hasfulltext | No Full Text | |
gro.griffith.author | Estivill-Castro, Vladimir | |
gro.griffith.author | Amirbekyan, Artak | |