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Secure and Efficient k-NN Queries.

Hafiz Asif1, Jaideep Vaidya1, Basit Shafiq2

  • 1MSIS Department, Rutgers University, USA.

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)
|December 9, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a secure k-NN computation protocol for financial portfolio matching. The novel approach enables efficient, privacy-preserving k-NN queries in distributed environments.

Keywords:
Distributed computationPrivacyk-NN classificationk-NN queries

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Area of Science:

  • Computer Science
  • Financial Technology
  • Cryptography

Background:

  • Ranking and best match queries are crucial for navigating large datasets.
  • k-Nearest Neighbors (k-NN) queries identify the k closest data points to a query, with broad applications.
  • The financial sector requires secure methods for querying sensitive investment portfolio data.

Purpose of the Study:

  • To develop a secure k-NN computation protocol for distributed multi-party environments.
  • To address the challenge of querying sensitive financial data while preserving privacy.
  • To incorporate domain semantics into secure k-NN computations for financial applications.

Main Methods:

  • Development of a novel secure k-NN computation protocol.
  • Implementation of the protocol in a distributed multi-party setting.
  • Evaluation of the protocol's efficiency and security in financial portfolio matching scenarios.

Main Results:

  • The proposed secure k-NN protocol demonstrated high efficiency in experimental tests.
  • The protocol successfully enabled secure k-NN queries on sensitive financial data.
  • Domain semantics were effectively integrated into the secure computation framework.

Conclusions:

  • The developed protocol offers an efficient and secure solution for k-NN queries in the financial sector.
  • This work advances privacy-preserving data analysis in distributed financial environments.
  • The protocol's efficiency and security make it suitable for real-world applications involving sensitive investment data.