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Related Concept Videos

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Atomic Nuclei: Nuclear Spin State Population Distribution01:14

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Related Experiment Video

Updated: Sep 7, 2025

Executing Complexity-Increasing Queries in Relational MySQL and NoSQL MongoDB and EXist Size-Growing ISO/EN 13606 Standardized EHR Databases
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Executing Complexity-Increasing Queries in Relational MySQL and NoSQL MongoDB and EXist Size-Growing ISO/EN 13606 Standardized EHR Databases

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Privacy-preserving k-NN interpolation over two encrypted databases.

Murat Osmanoglu1, Salih Demir1, Bulent Tugrul1

  • 1Department of Computer Engineering, Ankara University, Ankara, Turkey.

Peerj. Computer Science
|June 20, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a secure protocol for processing k-nearest neighbor queries across two encrypted cloud databases. This collaboration enhances accuracy and reliability while protecting data confidentiality and access patterns.

Keywords:
Big dataCloud computingInterpolationk-nearest neighbour

Related Experiment Videos

Last Updated: Sep 7, 2025

Executing Complexity-Increasing Queries in Relational MySQL and NoSQL MongoDB and EXist Size-Growing ISO/EN 13606 Standardized EHR Databases
07:26

Executing Complexity-Increasing Queries in Relational MySQL and NoSQL MongoDB and EXist Size-Growing ISO/EN 13606 Standardized EHR Databases

Published on: March 19, 2018

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Area of Science:

  • Cloud Computing Security
  • Database Management
  • Cryptography

Background:

  • Cloud computing offers benefits like cost savings and accessibility but raises security and privacy concerns.
  • Encrypted data in the cloud hinders complex operations like query processing.
  • Existing k-nearest neighbor algorithms on single encrypted databases face accuracy and reliability issues.

Purpose of the Study:

  • To address the limitations of single-database k-nearest neighbor queries in cloud environments.
  • To develop a secure protocol for collaborative query processing over multiple encrypted databases.
  • To enhance the accuracy and reliability of k-nearest neighbor search in cloud computing.

Main Methods:

  • Introduced a secure two-party k-nearest neighbor interpolation protocol.
  • Enabled query owners to extract k-nearest neighbors from two distinct encrypted databases.
  • Ensured data and query point confidentiality and hidden data access patterns.

Main Results:

  • The proposed protocol effectively processes k-nearest neighbor queries over two encrypted databases.
  • Demonstrated protection of data confidentiality, query point privacy, and access pattern obfuscation.
  • Experimental results confirmed the protocol's efficiency, with running time linearly dependent on neighbor count and data size.

Conclusions:

  • Collaborative query processing across multiple encrypted cloud databases improves accuracy and reliability.
  • The developed secure two-party protocol offers a viable solution for privacy-preserving k-nearest neighbor search in the cloud.
  • The protocol's linear scalability makes it suitable for large datasets and complex query requirements.