Hybrid approach of parallel implementation on CPU–GPU for high-speed ECDSA verification View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-01-10

AUTHORS

Sokjoon Lee, Hwajeong Seo, Hyeokchan Kwon, Hyunsoo Yoon

ABSTRACT

Since the advent of deep belief network deep learning technology in 2006, artificial intelligence technology has been utilized in various convergence areas, such as autonomous driving and medical care. Some services requiring fast decision making and action typically work seamlessly with edge computing service model. In autonomous driving of a connected vehicle with vehicle-to-everything (V2X) communication, roadside unit (RSU) acts as an edge computing device and it will make safer service by processing V2X messages fast, sent by vehicles or other devices. IEEE 1609.2 standard provides application message security technology to ensure the security and reliability of vehicle-to-vehicle communication messages. It uses elliptic curve digital signature algorithm (ECDSA) signatures based on the NIST p256 curve for message authenticity. In this paper, we investigate that RSU should be able to verify 3500 ECDSA signatures per second considering the expected maximum number of vehicles on nearby roads (e.g., during rush hour), message transmission rate, and IEEE 802.11p wireless channel capacity. RSU should satisfy this requirement without assistance of hardware-based cryptographic accelerator. For the requirement, we propose a hybrid approach of parallel ECDSA signature verification at high speed by using CPU and GPU, simultaneously. Moreover, we implemented the proposed method in various modern computing environments for RSU and edge computing devices. Through the experiments, we reach the conclusion that GPU can contribute to the required performance of ECDSA signature verification in RSU platform, which could not satisfy the above throughput only with CPU unit. The target platform with Intel Pentium E6500 CPU and GeForce GTX650 GPU can verify 5668 signatures per second with 30% utilization, while CPU in the platform can process only 2640 signatures. Even in a higher-performance edge computing device, we examine experimentally that the performance can be further improved by using the proposed hybrid approach. More... »

PAGES

1-21

References to SciGraph publications

  • 2015-02. Human-level control through deep reinforcement learning in NATURE
  • 2017-12. Multi-access edge computing: open issues, challenges and future perspectives in JOURNAL OF CLOUD COMPUTING
  • 2004. Aspects of Hyperelliptic Curves over Large Prime Fields in Software Implementations in CRYPTOGRAPHIC HARDWARE AND EMBEDDED SYSTEMS - CHES 2004
  • 2017-12. The convergence computing model for big sensor data mining and knowledge discovery in HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES
  • 2001. Software Implementation of the NIST Elliptic Curves Over Prime Fields in TOPICS IN CRYPTOLOGY — CT-RSA 2001
  • 2016-01. Mastering the game of Go with deep neural networks and tree search in NATURE
  • 2017-12. Optimization of sentiment analysis using machine learning classifiers in HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES
  • 2018-06. Implementation and Analysis of IEEE and ETSI Security Standards for Vehicular Communications in MOBILE NETWORKS AND APPLICATIONS
  • 2008. Ultra High Performance ECC over NIST Primes on Commercial FPGAs in CRYPTOGRAPHIC HARDWARE AND EMBEDDED SYSTEMS – CHES 2008
  • 2006. Curve25519: New Diffie-Hellman Speed Records in PUBLIC KEY CRYPTOGRAPHY - PKC 2006
  • 2014. A High-Speed Elliptic Curve Cryptographic Processor for Generic Curves over $$\mathrm{GF}(p)$$ in SELECTED AREAS IN CRYPTOGRAPHY -- SAC 2013
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s11227-019-02744-6

    DOI

    http://dx.doi.org/10.1007/s11227-019-02744-6

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1111317753


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Artificial Intelligence and Image Processing", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Information and Computing Sciences", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Electronics and Telecommunications Research Institute", 
              "id": "https://www.grid.ac/institutes/grid.36303.35", 
              "name": [
                "School of computing, KAIST, 291 Daehak-ro, Yuseong-gu, 34141, Daejeon, Korea", 
                "Information Security Research Division, ETRI, 218 Gajeong-ro, Yuseong-gu, 34129, Daejeon, Korea"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Lee", 
            "givenName": "Sokjoon", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Hansung University", 
              "id": "https://www.grid.ac/institutes/grid.444079.a", 
              "name": [
                "Department of IT, Hansung University, 116 Samseongyoro-16gil, Seongbuk-gu, 02876, Seoul, Korea"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Seo", 
            "givenName": "Hwajeong", 
            "id": "sg:person.015101423711.26", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015101423711.26"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Electronics and Telecommunications Research Institute", 
              "id": "https://www.grid.ac/institutes/grid.36303.35", 
              "name": [
                "Information Security Research Division, ETRI, 218 Gajeong-ro, Yuseong-gu, 34129, Daejeon, Korea"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Kwon", 
            "givenName": "Hyeokchan", 
            "id": "sg:person.013470301737.02", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013470301737.02"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Korea Advanced Institute of Science and Technology", 
              "id": "https://www.grid.ac/institutes/grid.37172.30", 
              "name": [
                "School of computing, KAIST, 291 Daehak-ro, Yuseong-gu, 34141, Daejeon, Korea"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Yoon", 
            "givenName": "Hyunsoo", 
            "id": "sg:person.010023517660.17", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010023517660.17"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "https://doi.org/10.1162/neco.2006.18.7.1527", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004707137"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/3-540-45353-9_19", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1021281712", 
              "https://doi.org/10.1007/3-540-45353-9_19"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature14236", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030517994", 
              "https://doi.org/10.1038/nature14236"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/11745853_14", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030714294", 
              "https://doi.org/10.1007/11745853_14"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/11745853_14", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030714294", 
              "https://doi.org/10.1007/11745853_14"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-662-43414-7_21", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1033316235", 
              "https://doi.org/10.1007/978-3-662-43414-7_21"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/1390156.1390177", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1035788679"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nature16961", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1039427823", 
              "https://doi.org/10.1038/nature16961"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-540-28632-5_11", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1040960314", 
              "https://doi.org/10.1007/978-3-540-28632-5_11"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-540-28632-5_11", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1040960314", 
              "https://doi.org/10.1007/978-3-540-28632-5_11"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-540-85053-3_5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1051927548", 
              "https://doi.org/10.1007/978-3-540-85053-3_5"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/comst.2015.2388550", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061258273"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/jiot.2014.2323395", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061280666"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/jiot.2016.2579198", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061280871"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/mc.2017.9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061389404"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/mcse.2010.69", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061398414"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/msp.2012.2205597", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061423808"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tdsc.2016.2577022", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061585617"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tvlsi.2016.2557965", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061817843"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tvt.2015.2487832", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061823367"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/comst.2017.2682318", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1084202545"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1177/1555343417695197", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1084247556"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1177/1555343417695197", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1084247556"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s13673-017-0092-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1084252702", 
              "https://doi.org/10.1186/s13673-017-0092-7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s13673-017-0092-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1084252702", 
              "https://doi.org/10.1186/s13673-017-0092-7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.4218/etrij.17.2816.0009", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1084456179"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.3745/jips.04.0029", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1085350041"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s13673-017-0116-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1091630354", 
              "https://doi.org/10.1186/s13673-017-0116-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tcsii.2017.2756680", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1091965219"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/cvpr.2016.90", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1093359587"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/vtcspring.2017.8108455", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1093485002"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/lcn.2010.5735781", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1093720371"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/iccv.2015.312", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094145536"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/icassp.2013.6638947", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1095157363"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.3115/v1/p14-5010", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1099127825"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.3115/v1/p14-5010", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1099127825"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/s13677-017-0097-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1099734313", 
              "https://doi.org/10.1186/s13677-017-0097-9"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11036-018-1019-x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1100746209", 
              "https://doi.org/10.1007/s11036-018-1019-x"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/vnc.2017.8275638", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1100767836"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.3390/s18041195", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1103237698"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2019-01-10", 
        "datePublishedReg": "2019-01-10", 
        "description": "Since the advent of deep belief network deep learning technology in 2006, artificial intelligence technology has been utilized in various convergence areas, such as autonomous driving and medical care. Some services requiring fast decision making and action typically work seamlessly with edge computing service model. In autonomous driving of a connected vehicle with vehicle-to-everything (V2X) communication, roadside unit (RSU) acts as an edge computing device and it will make safer service by processing V2X messages fast, sent by vehicles or other devices. IEEE 1609.2 standard provides application message security technology to ensure the security and reliability of vehicle-to-vehicle communication messages. It uses elliptic curve digital signature algorithm (ECDSA) signatures based on the NIST p256 curve for message authenticity. In this paper, we investigate that RSU should be able to verify 3500 ECDSA signatures per second considering the expected maximum number of vehicles on nearby roads (e.g., during rush hour), message transmission rate, and IEEE 802.11p wireless channel capacity. RSU should satisfy this requirement without assistance of hardware-based cryptographic accelerator. For the requirement, we propose a hybrid approach of parallel ECDSA signature verification at high speed by using CPU and GPU, simultaneously. Moreover, we implemented the proposed method in various modern computing environments for RSU and edge computing devices. Through the experiments, we reach the conclusion that GPU can contribute to the required performance of ECDSA signature verification in RSU platform, which could not satisfy the above throughput only with CPU unit. The target platform with Intel Pentium E6500 CPU and GeForce GTX650 GPU can verify 5668 signatures per second with 30% utilization, while CPU in the platform can process only 2640 signatures. Even in a higher-performance edge computing device, we examine experimentally that the performance can be further improved by using the proposed hybrid approach.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1007/s11227-019-02744-6", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": false, 
        "isPartOf": [
          {
            "id": "sg:journal.1133522", 
            "issn": [
              "0920-8542", 
              "1573-0484"
            ], 
            "name": "The Journal of Supercomputing", 
            "type": "Periodical"
          }
        ], 
        "name": "Hybrid approach of parallel implementation on CPU\u2013GPU for high-speed ECDSA verification", 
        "pagination": "1-21", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "384788276228eca193e00ae8016b8c3ddd1c6513dde0ae50d3bddb9251354a4c"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s11227-019-02744-6"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1111317753"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s11227-019-02744-6", 
          "https://app.dimensions.ai/details/publication/pub.1111317753"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-11T08:37", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000315_0000000315/records_6316_00000000.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://link.springer.com/10.1007%2Fs11227-019-02744-6"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s11227-019-02744-6'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s11227-019-02744-6'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11227-019-02744-6'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11227-019-02744-6'


     

    This table displays all metadata directly associated to this object as RDF triples.

    198 TRIPLES      21 PREDICATES      59 URIs      16 LITERALS      5 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s11227-019-02744-6 schema:about anzsrc-for:08
    2 anzsrc-for:0801
    3 schema:author N33a4110829844c3dbd9d323e8a898c62
    4 schema:citation sg:pub.10.1007/11745853_14
    5 sg:pub.10.1007/3-540-45353-9_19
    6 sg:pub.10.1007/978-3-540-28632-5_11
    7 sg:pub.10.1007/978-3-540-85053-3_5
    8 sg:pub.10.1007/978-3-662-43414-7_21
    9 sg:pub.10.1007/s11036-018-1019-x
    10 sg:pub.10.1038/nature14236
    11 sg:pub.10.1038/nature16961
    12 sg:pub.10.1186/s13673-017-0092-7
    13 sg:pub.10.1186/s13673-017-0116-3
    14 sg:pub.10.1186/s13677-017-0097-9
    15 https://doi.org/10.1109/comst.2015.2388550
    16 https://doi.org/10.1109/comst.2017.2682318
    17 https://doi.org/10.1109/cvpr.2016.90
    18 https://doi.org/10.1109/icassp.2013.6638947
    19 https://doi.org/10.1109/iccv.2015.312
    20 https://doi.org/10.1109/jiot.2014.2323395
    21 https://doi.org/10.1109/jiot.2016.2579198
    22 https://doi.org/10.1109/lcn.2010.5735781
    23 https://doi.org/10.1109/mc.2017.9
    24 https://doi.org/10.1109/mcse.2010.69
    25 https://doi.org/10.1109/msp.2012.2205597
    26 https://doi.org/10.1109/tcsii.2017.2756680
    27 https://doi.org/10.1109/tdsc.2016.2577022
    28 https://doi.org/10.1109/tvlsi.2016.2557965
    29 https://doi.org/10.1109/tvt.2015.2487832
    30 https://doi.org/10.1109/vnc.2017.8275638
    31 https://doi.org/10.1109/vtcspring.2017.8108455
    32 https://doi.org/10.1145/1390156.1390177
    33 https://doi.org/10.1162/neco.2006.18.7.1527
    34 https://doi.org/10.1177/1555343417695197
    35 https://doi.org/10.3115/v1/p14-5010
    36 https://doi.org/10.3390/s18041195
    37 https://doi.org/10.3745/jips.04.0029
    38 https://doi.org/10.4218/etrij.17.2816.0009
    39 schema:datePublished 2019-01-10
    40 schema:datePublishedReg 2019-01-10
    41 schema:description Since the advent of deep belief network deep learning technology in 2006, artificial intelligence technology has been utilized in various convergence areas, such as autonomous driving and medical care. Some services requiring fast decision making and action typically work seamlessly with edge computing service model. In autonomous driving of a connected vehicle with vehicle-to-everything (V2X) communication, roadside unit (RSU) acts as an edge computing device and it will make safer service by processing V2X messages fast, sent by vehicles or other devices. IEEE 1609.2 standard provides application message security technology to ensure the security and reliability of vehicle-to-vehicle communication messages. It uses elliptic curve digital signature algorithm (ECDSA) signatures based on the NIST p256 curve for message authenticity. In this paper, we investigate that RSU should be able to verify 3500 ECDSA signatures per second considering the expected maximum number of vehicles on nearby roads (e.g., during rush hour), message transmission rate, and IEEE 802.11p wireless channel capacity. RSU should satisfy this requirement without assistance of hardware-based cryptographic accelerator. For the requirement, we propose a hybrid approach of parallel ECDSA signature verification at high speed by using CPU and GPU, simultaneously. Moreover, we implemented the proposed method in various modern computing environments for RSU and edge computing devices. Through the experiments, we reach the conclusion that GPU can contribute to the required performance of ECDSA signature verification in RSU platform, which could not satisfy the above throughput only with CPU unit. The target platform with Intel Pentium E6500 CPU and GeForce GTX650 GPU can verify 5668 signatures per second with 30% utilization, while CPU in the platform can process only 2640 signatures. Even in a higher-performance edge computing device, we examine experimentally that the performance can be further improved by using the proposed hybrid approach.
    42 schema:genre research_article
    43 schema:inLanguage en
    44 schema:isAccessibleForFree false
    45 schema:isPartOf sg:journal.1133522
    46 schema:name Hybrid approach of parallel implementation on CPU–GPU for high-speed ECDSA verification
    47 schema:pagination 1-21
    48 schema:productId N3b8d9e3e10f244d0aec5099e31e1a948
    49 N55e329939dda479c985ced35e521a594
    50 N76c3b988edb4452995f5b0ac87346e10
    51 schema:sameAs https://app.dimensions.ai/details/publication/pub.1111317753
    52 https://doi.org/10.1007/s11227-019-02744-6
    53 schema:sdDatePublished 2019-04-11T08:37
    54 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    55 schema:sdPublisher N184330d5c26b4f6ebbadc75ae4d41bc6
    56 schema:url https://link.springer.com/10.1007%2Fs11227-019-02744-6
    57 sgo:license sg:explorer/license/
    58 sgo:sdDataset articles
    59 rdf:type schema:ScholarlyArticle
    60 N184330d5c26b4f6ebbadc75ae4d41bc6 schema:name Springer Nature - SN SciGraph project
    61 rdf:type schema:Organization
    62 N255f4c0540a6412b96b6a60f67d67420 rdf:first sg:person.013470301737.02
    63 rdf:rest N4e7be7dcf29448bebbd060f4eb197092
    64 N33a4110829844c3dbd9d323e8a898c62 rdf:first N67394f64ee194003b99bef9d056bc6ae
    65 rdf:rest Ndadfede6aa4749d9aa56ce957c6cd53a
    66 N3b8d9e3e10f244d0aec5099e31e1a948 schema:name dimensions_id
    67 schema:value pub.1111317753
    68 rdf:type schema:PropertyValue
    69 N4e7be7dcf29448bebbd060f4eb197092 rdf:first sg:person.010023517660.17
    70 rdf:rest rdf:nil
    71 N55e329939dda479c985ced35e521a594 schema:name doi
    72 schema:value 10.1007/s11227-019-02744-6
    73 rdf:type schema:PropertyValue
    74 N67394f64ee194003b99bef9d056bc6ae schema:affiliation https://www.grid.ac/institutes/grid.36303.35
    75 schema:familyName Lee
    76 schema:givenName Sokjoon
    77 rdf:type schema:Person
    78 N76c3b988edb4452995f5b0ac87346e10 schema:name readcube_id
    79 schema:value 384788276228eca193e00ae8016b8c3ddd1c6513dde0ae50d3bddb9251354a4c
    80 rdf:type schema:PropertyValue
    81 Ndadfede6aa4749d9aa56ce957c6cd53a rdf:first sg:person.015101423711.26
    82 rdf:rest N255f4c0540a6412b96b6a60f67d67420
    83 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    84 schema:name Information and Computing Sciences
    85 rdf:type schema:DefinedTerm
    86 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
    87 schema:name Artificial Intelligence and Image Processing
    88 rdf:type schema:DefinedTerm
    89 sg:journal.1133522 schema:issn 0920-8542
    90 1573-0484
    91 schema:name The Journal of Supercomputing
    92 rdf:type schema:Periodical
    93 sg:person.010023517660.17 schema:affiliation https://www.grid.ac/institutes/grid.37172.30
    94 schema:familyName Yoon
    95 schema:givenName Hyunsoo
    96 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010023517660.17
    97 rdf:type schema:Person
    98 sg:person.013470301737.02 schema:affiliation https://www.grid.ac/institutes/grid.36303.35
    99 schema:familyName Kwon
    100 schema:givenName Hyeokchan
    101 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013470301737.02
    102 rdf:type schema:Person
    103 sg:person.015101423711.26 schema:affiliation https://www.grid.ac/institutes/grid.444079.a
    104 schema:familyName Seo
    105 schema:givenName Hwajeong
    106 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015101423711.26
    107 rdf:type schema:Person
    108 sg:pub.10.1007/11745853_14 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030714294
    109 https://doi.org/10.1007/11745853_14
    110 rdf:type schema:CreativeWork
    111 sg:pub.10.1007/3-540-45353-9_19 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021281712
    112 https://doi.org/10.1007/3-540-45353-9_19
    113 rdf:type schema:CreativeWork
    114 sg:pub.10.1007/978-3-540-28632-5_11 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040960314
    115 https://doi.org/10.1007/978-3-540-28632-5_11
    116 rdf:type schema:CreativeWork
    117 sg:pub.10.1007/978-3-540-85053-3_5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051927548
    118 https://doi.org/10.1007/978-3-540-85053-3_5
    119 rdf:type schema:CreativeWork
    120 sg:pub.10.1007/978-3-662-43414-7_21 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033316235
    121 https://doi.org/10.1007/978-3-662-43414-7_21
    122 rdf:type schema:CreativeWork
    123 sg:pub.10.1007/s11036-018-1019-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1100746209
    124 https://doi.org/10.1007/s11036-018-1019-x
    125 rdf:type schema:CreativeWork
    126 sg:pub.10.1038/nature14236 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030517994
    127 https://doi.org/10.1038/nature14236
    128 rdf:type schema:CreativeWork
    129 sg:pub.10.1038/nature16961 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039427823
    130 https://doi.org/10.1038/nature16961
    131 rdf:type schema:CreativeWork
    132 sg:pub.10.1186/s13673-017-0092-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084252702
    133 https://doi.org/10.1186/s13673-017-0092-7
    134 rdf:type schema:CreativeWork
    135 sg:pub.10.1186/s13673-017-0116-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091630354
    136 https://doi.org/10.1186/s13673-017-0116-3
    137 rdf:type schema:CreativeWork
    138 sg:pub.10.1186/s13677-017-0097-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1099734313
    139 https://doi.org/10.1186/s13677-017-0097-9
    140 rdf:type schema:CreativeWork
    141 https://doi.org/10.1109/comst.2015.2388550 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061258273
    142 rdf:type schema:CreativeWork
    143 https://doi.org/10.1109/comst.2017.2682318 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084202545
    144 rdf:type schema:CreativeWork
    145 https://doi.org/10.1109/cvpr.2016.90 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093359587
    146 rdf:type schema:CreativeWork
    147 https://doi.org/10.1109/icassp.2013.6638947 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095157363
    148 rdf:type schema:CreativeWork
    149 https://doi.org/10.1109/iccv.2015.312 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094145536
    150 rdf:type schema:CreativeWork
    151 https://doi.org/10.1109/jiot.2014.2323395 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061280666
    152 rdf:type schema:CreativeWork
    153 https://doi.org/10.1109/jiot.2016.2579198 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061280871
    154 rdf:type schema:CreativeWork
    155 https://doi.org/10.1109/lcn.2010.5735781 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093720371
    156 rdf:type schema:CreativeWork
    157 https://doi.org/10.1109/mc.2017.9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061389404
    158 rdf:type schema:CreativeWork
    159 https://doi.org/10.1109/mcse.2010.69 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061398414
    160 rdf:type schema:CreativeWork
    161 https://doi.org/10.1109/msp.2012.2205597 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061423808
    162 rdf:type schema:CreativeWork
    163 https://doi.org/10.1109/tcsii.2017.2756680 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091965219
    164 rdf:type schema:CreativeWork
    165 https://doi.org/10.1109/tdsc.2016.2577022 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061585617
    166 rdf:type schema:CreativeWork
    167 https://doi.org/10.1109/tvlsi.2016.2557965 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061817843
    168 rdf:type schema:CreativeWork
    169 https://doi.org/10.1109/tvt.2015.2487832 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061823367
    170 rdf:type schema:CreativeWork
    171 https://doi.org/10.1109/vnc.2017.8275638 schema:sameAs https://app.dimensions.ai/details/publication/pub.1100767836
    172 rdf:type schema:CreativeWork
    173 https://doi.org/10.1109/vtcspring.2017.8108455 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093485002
    174 rdf:type schema:CreativeWork
    175 https://doi.org/10.1145/1390156.1390177 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035788679
    176 rdf:type schema:CreativeWork
    177 https://doi.org/10.1162/neco.2006.18.7.1527 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004707137
    178 rdf:type schema:CreativeWork
    179 https://doi.org/10.1177/1555343417695197 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084247556
    180 rdf:type schema:CreativeWork
    181 https://doi.org/10.3115/v1/p14-5010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1099127825
    182 rdf:type schema:CreativeWork
    183 https://doi.org/10.3390/s18041195 schema:sameAs https://app.dimensions.ai/details/publication/pub.1103237698
    184 rdf:type schema:CreativeWork
    185 https://doi.org/10.3745/jips.04.0029 schema:sameAs https://app.dimensions.ai/details/publication/pub.1085350041
    186 rdf:type schema:CreativeWork
    187 https://doi.org/10.4218/etrij.17.2816.0009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084456179
    188 rdf:type schema:CreativeWork
    189 https://www.grid.ac/institutes/grid.36303.35 schema:alternateName Electronics and Telecommunications Research Institute
    190 schema:name Information Security Research Division, ETRI, 218 Gajeong-ro, Yuseong-gu, 34129, Daejeon, Korea
    191 School of computing, KAIST, 291 Daehak-ro, Yuseong-gu, 34141, Daejeon, Korea
    192 rdf:type schema:Organization
    193 https://www.grid.ac/institutes/grid.37172.30 schema:alternateName Korea Advanced Institute of Science and Technology
    194 schema:name School of computing, KAIST, 291 Daehak-ro, Yuseong-gu, 34141, Daejeon, Korea
    195 rdf:type schema:Organization
    196 https://www.grid.ac/institutes/grid.444079.a schema:alternateName Hansung University
    197 schema:name Department of IT, Hansung University, 116 Samseongyoro-16gil, Seongbuk-gu, 02876, Seoul, Korea
    198 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...