Evidence map›Paper›PMID 29218333›Full record

ArticleICT systems security and privacy protection : 32nd IFIP TC 11 International Conference, SEC 2017, Rome, Italy, May 29-31, 2017, Proceedings. IFIP TC11 International Information Security Conference (32nd : 2017 : Rome, Italy)2017

Secure and Efficient k-NN Queries.

Hafiz Asif, Jaideep Vaidya, Basit Shafiq, Nabil Adam

Abstract read
In one paragraph

Article in ICT systems security and privacy protection : 32nd IFIP TC 11 International Conference, SEC 2017, Rome, Italy, May 29-31, 2017, Proceedings. IFIP TC11 International Information Security Conference (32nd : 2017 : Rome, Italy), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Hafiz AsifMSIS Department, Rutgers University, USA.
Jaideep VaidyaMSIS Department, Rutgers University, USA.
Basit ShafiqCS Department, Lahore Univeristy of Managment Sciences, Pakistan.
Nabil AdamMSIS Department, Rutgers University, USA.

Funding

Secure and PrivAte Collaborative EnvironmentS (SPACES) for biomedical analyticsR01GM118574 · NIGMS · RUTGERS THE STATE UNIV OF NJ NEWARK · PI VAIDYA, JAIDEEP · 2017 to 2019
$1.1M
NIGMS NIH HHS R01 GM118574
6 · The paper itself

Abstract

Given the morass of available data, ranking and best match queries are often used to find records of interest. As such, k-NN queries, which give the k closest matches to a query point, are of particular interest, and have many applications. We study this problem in the context of the financial sector, wherein an investment portfolio database is queried for matching portfolios. Given the sensitivity of the information involved, our key contribution is to develop a secure k-NN computation protocol that can enable the computation k-NN queries in a distributed multi-party environment while taking domain semantics into account. The experimental results show that the proposed protocols are extremely efficient.

Indexed as

Distributed computationk-NN classificationk-NN queriesPrivacy

Identifiers

PMID29218333
PMCPMC5713910

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.