Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
SEECC: A Secure and Efficient Elliptic Curve Cryptosystem for E-health Applications
KTH, School of Information and Communication Technology (ICT), Elektronics, Integrated devices and circuits. Univ Turku, Finland.
Show others and affiliations
2016 (English)In: 2016 INTERNATIONAL CONFERENCE ON HIGH PERFORMANCE COMPUTING & SIMULATION (HPCS 2016), IEEE, 2016, p. 492-500Conference paper, Published paper (Refereed)
Abstract [en]

Security is an essential factor in wireless sensor networks especially for E-health applications. One of the common mechanisms to satisfy the security requirements is cryptography. Among the cryptographic methods, elliptic curve cryptography is well-known, as by having a small key length it provides the same security level in comparison with the other public key cryptosystems. The small key sizes make ECC very interesting for devices with limited processing power or memory such as wearable devices for E-health applications. It is vitally important that elliptic curves are protected against all kinds of attacks concerning the security of elliptic curve cryptography. Selection of a secure elliptic curve is a mathematically difficult problem. In this paper, an efficient elliptic curve selection framework, called SEECC, is proposed to select a secure and efficient curve front all the available elliptic curves. This method enhances the security and efficiency of elliptic curve cryptosystems by using a parallel genetic algorithm.

Place, publisher, year, edition, pages
IEEE, 2016. p. 492-500
Keywords [en]
elliptic curve cryptography, secure elliptic curve, evolutionary computing, genetic algorithms, parallel genetic algorithms, multi population parallel genetic algorithms, E-health
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-200031DOI: 10.1109/HPCSim.2016.7568375ISI: 000389590600067Scopus ID: 2-s2.0-84991640202ISBN: 978-1-5090-2088-1 (print)OAI: oai:DiVA.org:kth-200031DiVA, id: diva2:1069592
Conference
14th International Conference on High Performance Computing & Simulation (HPCS), JUL 18-22, 2016, Innsbruck, AUSTRIA
Note

QC 20170130

Available from: 2017-01-30 Created: 2017-01-20 Last updated: 2017-01-30Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Ebrahimi, MasoumehTenhunen, Hannu

Search in DiVA

By author/editor
Ebrahimi, MasoumehTenhunen, Hannu
By organisation
Integrated devices and circuits
Electrical Engineering, Electronic Engineering, Information Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 9 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf