Secret sharing approach for securing cloud-based pre-classification volume ray-casting View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2016-06

AUTHORS

Manoranjan Mohanty, Wei Tsang Ooi, Pradeep K. Atrey

ABSTRACT

With the evolution in cloud computing, cloud-based volume rendering, which outsources data rendering tasks to cloud datacenters, is attracting interest. Although this new rendering technique has many advantages, allowing third-party access to potentially sensitive volume data raises security and privacy concerns. In this paper, we address these concerns for cloud-based pre-classification volume ray-casting by using Shamir’s (k, n) secret sharing and its variant (l, k, n) ramp secret sharing, which are homomorphic to addition and scalar multiplication operations, to hide color information of volume data/images in datacenters. To address the incompatibility issue of the modular prime operation used in secret sharing technique with the floating point operations of ray-casting, we consider excluding modular prime operation from secret sharing or converting the floating number operations of ray-casting to fixed point operations – the earlier technique degrades security and the later degrades image quality. Both these techniques, however, result in significant data overhead. To lessen the overhead at the cost of high security, we propose a modified ramp secret sharing scheme that uses the three color components in one secret sharing polynomial and replaces the shares in floating point with smaller integers. More... »

PAGES

6207-6235

References to SciGraph publications

  • 2012. A Cloud Computing Medical Image Analysis and Collaboration Platform in CLOUD COMPUTING AND SERVICES SCIENCE
  • 1993-06. Secret sharing over infinite domains in JOURNAL OF CRYPTOLOGY
  • 2010. Secure Computation with Fixed-Point Numbers in FINANCIAL CRYPTOGRAPHY AND DATA SECURITY
  • 2010. Cryptographic Cloud Storage in FINANCIAL CRYPTOGRAPHY AND DATA SECURITY
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s11042-015-2567-8

    DOI

    http://dx.doi.org/10.1007/s11042-015-2567-8

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1049728574


    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": "Swedish Institute of Computer Science", 
              "id": "https://www.grid.ac/institutes/grid.6383.e", 
              "name": [
                "Security Lab, SICS Swedish ICT, Kista, Sweden"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Mohanty", 
            "givenName": "Manoranjan", 
            "id": "sg:person.016003652640.68", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016003652640.68"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "National University of Singapore", 
              "id": "https://www.grid.ac/institutes/grid.4280.e", 
              "name": [
                "Department of Computer Science, National University of Singapore, Singapore, Singapore"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Ooi", 
            "givenName": "Wei Tsang", 
            "id": "sg:person.010444710761.13", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010444710761.13"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "University at Albany, State University of New York", 
              "id": "https://www.grid.ac/institutes/grid.265850.c", 
              "name": [
                "Department of Computer Science, University at Albany - State University of New York, Albany, NY, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Atrey", 
            "givenName": "Pradeep K.", 
            "id": "sg:person.014721056635.29", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014721056635.29"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "https://doi.org/10.1016/j.future.2010.12.006", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006597960"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-14577-3_6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1010984377", 
              "https://doi.org/10.1007/978-3-642-14577-3_6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-14577-3_6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1010984377", 
              "https://doi.org/10.1007/978-3-642-14577-3_6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.neurobiolaging.2004.11.006", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1011006441"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/2393347.2396394", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1015226470"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-1-4614-2326-3_11", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1018032260", 
              "https://doi.org/10.1007/978-1-4614-2326-3_11"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/359168.359176", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036015253"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/2046660.2046682", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036932326"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf02620136", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1043618034", 
              "https://doi.org/10.1007/bf02620136"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf02620136", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1043618034", 
              "https://doi.org/10.1007/bf02620136"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/2160749.2160800", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1045396811"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-14992-4_13", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1048347583", 
              "https://doi.org/10.1007/978-3-642-14992-4_13"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-14992-4_13", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1048347583", 
              "https://doi.org/10.1007/978-3-642-14992-4_13"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/38.511", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061163896"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tc.2010.40", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061535006"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tvcg.2009.164", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061813178"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/iihmsp.2010.130", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1093591784"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/seaa.2011.31", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1093756758"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/hicss.2012.153", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1093820873"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/cloudcom.2013.77", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094052208"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/icsc.2013.60", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094796857"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2016-06", 
        "datePublishedReg": "2016-06-01", 
        "description": "With the evolution in cloud computing, cloud-based volume rendering, which outsources data rendering tasks to cloud datacenters, is attracting interest. Although this new rendering technique has many advantages, allowing third-party access to potentially sensitive volume data raises security and privacy concerns. In this paper, we address these concerns for cloud-based pre-classification volume ray-casting by using Shamir\u2019s (k, n) secret sharing and its variant (l, k, n) ramp secret sharing, which are homomorphic to addition and scalar multiplication operations, to hide color information of volume data/images in datacenters. To address the incompatibility issue of the modular prime operation used in secret sharing technique with the floating point operations of ray-casting, we consider excluding modular prime operation from secret sharing or converting the floating number operations of ray-casting to fixed point operations \u2013 the earlier technique degrades security and the later degrades image quality. Both these techniques, however, result in significant data overhead. To lessen the overhead at the cost of high security, we propose a modified ramp secret sharing scheme that uses the three color components in one secret sharing polynomial and replaces the shares in floating point with smaller integers.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1007/s11042-015-2567-8", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": false, 
        "isFundedItemOf": [
          {
            "id": "sg:grant.7708132", 
            "type": "MonetaryGrant"
          }, 
          {
            "id": "sg:grant.2857379", 
            "type": "MonetaryGrant"
          }
        ], 
        "isPartOf": [
          {
            "id": "sg:journal.1044869", 
            "issn": [
              "1380-7501", 
              "1573-7721"
            ], 
            "name": "Multimedia Tools and Applications", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "11", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "75"
          }
        ], 
        "name": "Secret sharing approach for securing cloud-based pre-classification volume ray-casting", 
        "pagination": "6207-6235", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "ece3f24328ad1ec561dc713bc19ed48bd176551d046b8c4ed88d1b11fabb0578"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s11042-015-2567-8"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1049728574"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s11042-015-2567-8", 
          "https://app.dimensions.ai/details/publication/pub.1049728574"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-10T16:43", 
        "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/v90/0000000001_0000000264/records_8669_00000516.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "http://link.springer.com/10.1007%2Fs11042-015-2567-8"
      }
    ]
     

    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/s11042-015-2567-8'

    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/s11042-015-2567-8'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11042-015-2567-8'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11042-015-2567-8'


     

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

    143 TRIPLES      21 PREDICATES      45 URIs      19 LITERALS      7 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s11042-015-2567-8 schema:about anzsrc-for:08
    2 anzsrc-for:0801
    3 schema:author N48c964f6b9624a939b595665ca99401c
    4 schema:citation sg:pub.10.1007/978-1-4614-2326-3_11
    5 sg:pub.10.1007/978-3-642-14577-3_6
    6 sg:pub.10.1007/978-3-642-14992-4_13
    7 sg:pub.10.1007/bf02620136
    8 https://doi.org/10.1016/j.future.2010.12.006
    9 https://doi.org/10.1016/j.neurobiolaging.2004.11.006
    10 https://doi.org/10.1109/38.511
    11 https://doi.org/10.1109/cloudcom.2013.77
    12 https://doi.org/10.1109/hicss.2012.153
    13 https://doi.org/10.1109/icsc.2013.60
    14 https://doi.org/10.1109/iihmsp.2010.130
    15 https://doi.org/10.1109/seaa.2011.31
    16 https://doi.org/10.1109/tc.2010.40
    17 https://doi.org/10.1109/tvcg.2009.164
    18 https://doi.org/10.1145/2046660.2046682
    19 https://doi.org/10.1145/2160749.2160800
    20 https://doi.org/10.1145/2393347.2396394
    21 https://doi.org/10.1145/359168.359176
    22 schema:datePublished 2016-06
    23 schema:datePublishedReg 2016-06-01
    24 schema:description With the evolution in cloud computing, cloud-based volume rendering, which outsources data rendering tasks to cloud datacenters, is attracting interest. Although this new rendering technique has many advantages, allowing third-party access to potentially sensitive volume data raises security and privacy concerns. In this paper, we address these concerns for cloud-based pre-classification volume ray-casting by using Shamir’s (k, n) secret sharing and its variant (l, k, n) ramp secret sharing, which are homomorphic to addition and scalar multiplication operations, to hide color information of volume data/images in datacenters. To address the incompatibility issue of the modular prime operation used in secret sharing technique with the floating point operations of ray-casting, we consider excluding modular prime operation from secret sharing or converting the floating number operations of ray-casting to fixed point operations – the earlier technique degrades security and the later degrades image quality. Both these techniques, however, result in significant data overhead. To lessen the overhead at the cost of high security, we propose a modified ramp secret sharing scheme that uses the three color components in one secret sharing polynomial and replaces the shares in floating point with smaller integers.
    25 schema:genre research_article
    26 schema:inLanguage en
    27 schema:isAccessibleForFree false
    28 schema:isPartOf N10a4df915d9243b793334e35a00dcec5
    29 N20619fb507e04775b37444896be3af8d
    30 sg:journal.1044869
    31 schema:name Secret sharing approach for securing cloud-based pre-classification volume ray-casting
    32 schema:pagination 6207-6235
    33 schema:productId N793fd654d8d349fb933d2341cd3c848f
    34 Nacad265d589c4ef2993203ad3810aa1b
    35 Nfc1992e778dc4237aa1379869826aab0
    36 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049728574
    37 https://doi.org/10.1007/s11042-015-2567-8
    38 schema:sdDatePublished 2019-04-10T16:43
    39 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    40 schema:sdPublisher Nea5287efa78748ffa061d59c78535cee
    41 schema:url http://link.springer.com/10.1007%2Fs11042-015-2567-8
    42 sgo:license sg:explorer/license/
    43 sgo:sdDataset articles
    44 rdf:type schema:ScholarlyArticle
    45 N10a4df915d9243b793334e35a00dcec5 schema:volumeNumber 75
    46 rdf:type schema:PublicationVolume
    47 N12ca0cc6786f46829e0c1e271a2a4716 rdf:first sg:person.010444710761.13
    48 rdf:rest N29404c08bf374b49a2286831ce36ddfa
    49 N20619fb507e04775b37444896be3af8d schema:issueNumber 11
    50 rdf:type schema:PublicationIssue
    51 N29404c08bf374b49a2286831ce36ddfa rdf:first sg:person.014721056635.29
    52 rdf:rest rdf:nil
    53 N48c964f6b9624a939b595665ca99401c rdf:first sg:person.016003652640.68
    54 rdf:rest N12ca0cc6786f46829e0c1e271a2a4716
    55 N793fd654d8d349fb933d2341cd3c848f schema:name dimensions_id
    56 schema:value pub.1049728574
    57 rdf:type schema:PropertyValue
    58 Nacad265d589c4ef2993203ad3810aa1b schema:name readcube_id
    59 schema:value ece3f24328ad1ec561dc713bc19ed48bd176551d046b8c4ed88d1b11fabb0578
    60 rdf:type schema:PropertyValue
    61 Nea5287efa78748ffa061d59c78535cee schema:name Springer Nature - SN SciGraph project
    62 rdf:type schema:Organization
    63 Nfc1992e778dc4237aa1379869826aab0 schema:name doi
    64 schema:value 10.1007/s11042-015-2567-8
    65 rdf:type schema:PropertyValue
    66 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    67 schema:name Information and Computing Sciences
    68 rdf:type schema:DefinedTerm
    69 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
    70 schema:name Artificial Intelligence and Image Processing
    71 rdf:type schema:DefinedTerm
    72 sg:grant.2857379 http://pending.schema.org/fundedItem sg:pub.10.1007/s11042-015-2567-8
    73 rdf:type schema:MonetaryGrant
    74 sg:grant.7708132 http://pending.schema.org/fundedItem sg:pub.10.1007/s11042-015-2567-8
    75 rdf:type schema:MonetaryGrant
    76 sg:journal.1044869 schema:issn 1380-7501
    77 1573-7721
    78 schema:name Multimedia Tools and Applications
    79 rdf:type schema:Periodical
    80 sg:person.010444710761.13 schema:affiliation https://www.grid.ac/institutes/grid.4280.e
    81 schema:familyName Ooi
    82 schema:givenName Wei Tsang
    83 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010444710761.13
    84 rdf:type schema:Person
    85 sg:person.014721056635.29 schema:affiliation https://www.grid.ac/institutes/grid.265850.c
    86 schema:familyName Atrey
    87 schema:givenName Pradeep K.
    88 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014721056635.29
    89 rdf:type schema:Person
    90 sg:person.016003652640.68 schema:affiliation https://www.grid.ac/institutes/grid.6383.e
    91 schema:familyName Mohanty
    92 schema:givenName Manoranjan
    93 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016003652640.68
    94 rdf:type schema:Person
    95 sg:pub.10.1007/978-1-4614-2326-3_11 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018032260
    96 https://doi.org/10.1007/978-1-4614-2326-3_11
    97 rdf:type schema:CreativeWork
    98 sg:pub.10.1007/978-3-642-14577-3_6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010984377
    99 https://doi.org/10.1007/978-3-642-14577-3_6
    100 rdf:type schema:CreativeWork
    101 sg:pub.10.1007/978-3-642-14992-4_13 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048347583
    102 https://doi.org/10.1007/978-3-642-14992-4_13
    103 rdf:type schema:CreativeWork
    104 sg:pub.10.1007/bf02620136 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043618034
    105 https://doi.org/10.1007/bf02620136
    106 rdf:type schema:CreativeWork
    107 https://doi.org/10.1016/j.future.2010.12.006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006597960
    108 rdf:type schema:CreativeWork
    109 https://doi.org/10.1016/j.neurobiolaging.2004.11.006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011006441
    110 rdf:type schema:CreativeWork
    111 https://doi.org/10.1109/38.511 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061163896
    112 rdf:type schema:CreativeWork
    113 https://doi.org/10.1109/cloudcom.2013.77 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094052208
    114 rdf:type schema:CreativeWork
    115 https://doi.org/10.1109/hicss.2012.153 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093820873
    116 rdf:type schema:CreativeWork
    117 https://doi.org/10.1109/icsc.2013.60 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094796857
    118 rdf:type schema:CreativeWork
    119 https://doi.org/10.1109/iihmsp.2010.130 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093591784
    120 rdf:type schema:CreativeWork
    121 https://doi.org/10.1109/seaa.2011.31 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093756758
    122 rdf:type schema:CreativeWork
    123 https://doi.org/10.1109/tc.2010.40 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061535006
    124 rdf:type schema:CreativeWork
    125 https://doi.org/10.1109/tvcg.2009.164 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061813178
    126 rdf:type schema:CreativeWork
    127 https://doi.org/10.1145/2046660.2046682 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036932326
    128 rdf:type schema:CreativeWork
    129 https://doi.org/10.1145/2160749.2160800 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045396811
    130 rdf:type schema:CreativeWork
    131 https://doi.org/10.1145/2393347.2396394 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015226470
    132 rdf:type schema:CreativeWork
    133 https://doi.org/10.1145/359168.359176 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036015253
    134 rdf:type schema:CreativeWork
    135 https://www.grid.ac/institutes/grid.265850.c schema:alternateName University at Albany, State University of New York
    136 schema:name Department of Computer Science, University at Albany - State University of New York, Albany, NY, USA
    137 rdf:type schema:Organization
    138 https://www.grid.ac/institutes/grid.4280.e schema:alternateName National University of Singapore
    139 schema:name Department of Computer Science, National University of Singapore, Singapore, Singapore
    140 rdf:type schema:Organization
    141 https://www.grid.ac/institutes/grid.6383.e schema:alternateName Swedish Institute of Computer Science
    142 schema:name Security Lab, SICS Swedish ICT, Kista, Sweden
    143 rdf:type schema:Organization
     




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


    ...