Abstract
Multistep processing is commonly used for nearest neighbor (NN) and similarity search in applications involving high-dimensional data and/or costly distance computations. Today, many such applications require a proof of result correctness. In this setting, clients issue NN queries to a server that maintains a database signed by a trusted authority. The server returns the NN set along with supplementary information that permits result verification using the data set signature. An adaptation of the multistep NN algorithm incurs prohibitive network overhead due to the transmission of false hits, i.e., records that are not in the NN set, but are nevertheless necessary for its verification. In order to alleviate this problem, we present a novel technique that reduces the size of each false hit. Moreover, we generalize our solution for a distributed setting, where the database is horizontally partitioned over several servers. Finally, we demonstrate the effectiveness of the proposed solutions with real data sets of various dimensionalities.
| Original language | English |
|---|---|
| Article number | 5560661 |
| Pages (from-to) | 641-654 |
| Number of pages | 14 |
| Journal | IEEE Transactions on Knowledge and Data Engineering |
| Volume | 23 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - 2011 |
| Externally published | Yes |
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Keywords
- multistep nearest neighbors
- Query authentication
- similarity search
ASJC Scopus subject areas
- Information Systems
- Computer Science Applications
- Computational Theory and Mathematics
Cite this
Authenticated multistep nearest neighbor search. / Papadopoulos, Stavros; Wang, Lixing; Yang, Yin; Papadias, Dimitris; Karras, Panagiotis.
In: IEEE Transactions on Knowledge and Data Engineering, Vol. 23, No. 5, 5560661, 2011, p. 641-654.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Authenticated multistep nearest neighbor search
AU - Papadopoulos, Stavros
AU - Wang, Lixing
AU - Yang, Yin
AU - Papadias, Dimitris
AU - Karras, Panagiotis
PY - 2011
Y1 - 2011
N2 - Multistep processing is commonly used for nearest neighbor (NN) and similarity search in applications involving high-dimensional data and/or costly distance computations. Today, many such applications require a proof of result correctness. In this setting, clients issue NN queries to a server that maintains a database signed by a trusted authority. The server returns the NN set along with supplementary information that permits result verification using the data set signature. An adaptation of the multistep NN algorithm incurs prohibitive network overhead due to the transmission of false hits, i.e., records that are not in the NN set, but are nevertheless necessary for its verification. In order to alleviate this problem, we present a novel technique that reduces the size of each false hit. Moreover, we generalize our solution for a distributed setting, where the database is horizontally partitioned over several servers. Finally, we demonstrate the effectiveness of the proposed solutions with real data sets of various dimensionalities.
AB - Multistep processing is commonly used for nearest neighbor (NN) and similarity search in applications involving high-dimensional data and/or costly distance computations. Today, many such applications require a proof of result correctness. In this setting, clients issue NN queries to a server that maintains a database signed by a trusted authority. The server returns the NN set along with supplementary information that permits result verification using the data set signature. An adaptation of the multistep NN algorithm incurs prohibitive network overhead due to the transmission of false hits, i.e., records that are not in the NN set, but are nevertheless necessary for its verification. In order to alleviate this problem, we present a novel technique that reduces the size of each false hit. Moreover, we generalize our solution for a distributed setting, where the database is horizontally partitioned over several servers. Finally, we demonstrate the effectiveness of the proposed solutions with real data sets of various dimensionalities.
KW - multistep nearest neighbors
KW - Query authentication
KW - similarity search
UR - http://www.scopus.com/inward/record.url?scp=79953208596&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79953208596&partnerID=8YFLogxK
U2 - 10.1109/TKDE.2010.157
DO - 10.1109/TKDE.2010.157
M3 - Article
VL - 23
SP - 641
EP - 654
JO - IEEE Transactions on Knowledge and Data Engineering
JF - IEEE Transactions on Knowledge and Data Engineering
SN - 1041-4347
IS - 5
M1 - 5560661
ER -