Kernel Metadata Declaration — on demand

ISO 26324 asks a DOI Registration Agency to produce a Kernel Metadata Declaration for every DOI it issues. Give any DOI: its public record becomes a record in the Smart Scholars DOI Metadata Format 1.0, checked against every rule, and declared as the Kernel XML.

✓ Meets every rule of the format

The record below is complete and every controlled value is a DOI Attribute Value Set 2.3 spelling. The Declaration on the right follows DOIMetadataKernel.xsd element by element.

What the DOI identifies

PRESERVING PRIVACY IN THE ERA OF BIG DATA A ML BASED ANONYMIZATION FRAMEWORK

10.46243/jst.2023.v8.i12.pp139-146 · JournalArticle — an article in a journal · Digital · Visual · Language · en

Published 2023-12-12

Part of Journal of Science & Technology · ISSN 2456-5660 · vol. 8 · no. 12 · pp. 139–146

Principal agents

Aarthi Kasthuri Aarthi Kasthuri (author → Author)
Longman Publishers (publisher → Publisher)

Also in the record, outside the Kernel: the abstract, the licence, 3 links, 0 references. Source: Crossref (member 25296), registered 2024-02-16, last deposited 2026-09-28.

⬇ Record (JSON) ⬇ Declaration (XML)

System metadata — ISO 26324:2025, Annex B · DOI Handbook 10.1

Each element by the standard's name and the Handbook's (in grey), read off the record.

DOI Name DOI name	10.46243/jst.2023.v8.i12.pp139-146
Referent Type referentType	Creation
Referent Sub-Type referentSubType	JournalArticle — an article in a journal
Referent Name(s) referentName(s)	PRESERVING PRIVACY IN THE ERA OF BIG DATA A ML BASED ANONYMIZATION FRAMEWORK (PrincipalTitle, en)
Basic Metadata basicMetadata	author: Aarthi Kasthuri Aarthi Kasthuri publisher: Longman Publishers published: 2023-12-12 part of: Journal of Science & Technology · ISSN 2456-5660 · vol. 8 · no. 12 · pp. 139–146 language: en form: Digital · Visual · Language
Referent Identifier(s) alternateIdentifier(s)	none besides the DOI
Registration Authority registrationAuthorityCode	Crossref — issued by Crossref (member 25296); held here as a copy
Created Date issueDate	2024-02-16
relatedIdentifiers	none needed — the descriptive metadata is in this record

The record

{
    "format": "smartscholars-doi-metadata/1.0",
    "doi": "10.46243/jst.2023.v8.i12.pp139-146",
    "referent": "Creation",
    "type": "JournalArticle",
    "structural_type": "Digital",
    "modes": [
        "Visual"
    ],
    "characters": [
        "Language"
    ],
    "titles": [
        {
            "value": "PRESERVING PRIVACY IN THE ERA OF BIG DATA A ML BASED ANONYMIZATION FRAMEWORK",
            "type": "PrincipalTitle",
            "lang": "en"
        }
    ],
    "identifiers": [
        {
            "type": "DOI",
            "value": "10.46243/jst.2023.v8.i12.pp139-146"
        }
    ],
    "agents": [
        {
            "role": "author",
            "name": {
                "given": "Aarthi Kasthuri",
                "family": "Aarthi Kasthuri"
            },
            "sequence": "first"
        },
        {
            "role": "publisher",
            "name": {
                "org": "Longman Publishers"
            }
        }
    ],
    "dates": {
        "published": "2023-12-12",
        "date_type": "PublicationDate",
        "online": "2023-12-12"
    },
    "language": "en",
    "container": {
        "type": "Journal",
        "titles": [
            {
                "value": "Journal of Science & Technology",
                "type": "PrincipalTitle"
            }
        ],
        "identifiers": [
            {
                "type": "ISSN",
                "value": "2456-5660",
                "medium": "electronic"
            }
        ],
        "volume": "8",
        "issue": "12",
        "pages": {
            "first": "139",
            "last": "146"
        }
    },
    "links": [
        {
            "url": "https://www.jst.org.in/index.php/pub/article/view/874",
            "return_type": "text/html",
            "primary": true
        },
        {
            "url": "https://www.jst.org.in/index.php/pub/article/download/874/802",
            "purpose": "text-mining",
            "return_type": "application/pdf"
        },
        {
            "url": "https://www.jst.org.in/index.php/pub/article/download/874/1685",
            "purpose": "text-mining",
            "return_type": "application/xml"
        }
    ],
    "abstract": {
        "value": "Publishing datasets plays an essential role in open data research and promoting transparency of government agencies. However, such data publication might reveal users’ private information. One of the most sensitive sources of data is spatiotemporal trajectory datasets. Unfortunately, merely removing unique identifiers cannot preserve the privacy of users. Adversaries may know parts of the trajectories or be able to link the published dataset to other sources for the purpose of user identification. Therefore, it is crucial to apply privacy preserving techniques before the publication of spatiotemporal trajectory datasets. In this paper, we propose a robust framework for the anonymization of spatiotemporal trajectory datasets termed as machine learning based anonymization (MLA). By introducing a new formulation of the problem, we are able to apply machine learning algorithms for clustering the trajectories and propose to use k-means algorithm for this purpose. A variation of k-means algorithm is also proposed to preserve the privacy in overly sensitive datasets. Moreover, we improve the alignment process by considering multiple sequence alignment as part of the MLA. The framework and all the proposed algorithms are applied to T-Drive, Geolife, and Gowalla location datasets. The experimental results indicate a significantly higher utility of datasets by anonymization based on MLA framework.",
        "lang": "en"
    },
    "license": {
        "url": "https://creativecommons.org/licenses/by/4.0/",
        "start": "2023-12-12",
        "applies_to": "vor"
    },
    "record": {
        "registrant": "Longman Publishers",
        "registered": "2024-02-16",
        "updated": "2026-09-28",
        "issue_number": 1,
        "source": "crossref-api",
        "source_agency": "Crossref (member 25296)"
    }
}

The Kernel Metadata Declaration

DOIMetadataKernel.xsd · namespace http://www.doi.org/2010/DOISchema · Attribute Value Sets 2.3 · draft — the agency's DOI name is filled in on accreditation

<?xml version="1.0" encoding="UTF-8"?>
<!-- DRAFT declaration: Smart Scholars is not yet a DOI Registration Agency. registrationAgencyDoiName is a marker (10.0/ is no RA's prefix) and is filled in on accreditation. -->
<kernelMetadata xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.doi.org/2010/DOISchema https://www.doi.org/doi_schemas/DOIMetadataKernel.xsd" xmlns="http://www.doi.org/2010/DOISchema">
  <referentDoiName>10.46243/jst.2023.v8.i12.pp139-146</referentDoiName>
  <primaryReferentType>Creation</primaryReferentType>
  <registrationAgencyDoiName>10.0/smart-scholars-draft</registrationAgencyDoiName>
  <issueDate>2026-10-04</issueDate>
  <issueNumber>1</issueNumber>
  <referentCreation>
    <name primaryLanguage="en">
      <value>PRESERVING PRIVACY IN THE ERA OF BIG DATA A ML BASED ANONYMIZATION FRAMEWORK</value>
      <type>PrincipalTitle</type>
    </name>
    <identifier>
      <nonUriValue>10.46243/jst.2023.v8.i12.pp139-146</nonUriValue>
      <uri returnType="text/html" doesContentNegotiation="true">https://doi.org/10.46243/jst.2023.v8.i12.pp139-146</uri>
      <type>DOI</type>
    </identifier>
    <identifier>
      <uri returnType="text/html">https://www.jst.org.in/index.php/pub/article/view/874</uri>
      <type>URI</type>
    </identifier>
    <identifier>
      <uri>https://www.jst.org.in/index.php/pub/article/download/874/802</uri>
      <type>URI</type>
    </identifier>
    <identifier>
      <uri returnType="application/xml">https://www.jst.org.in/index.php/pub/article/download/874/1685</uri>
      <type>URI</type>
    </identifier>
    <structuralType>Digital</structuralType>
    <mode>Visual</mode>
    <character>Language</character>
    <type>JournalArticle</type>
    <principalAgent>
      <name>
        <value>Aarthi Kasthuri Aarthi Kasthuri</value>
        <type>PrincipalName</type>
      </name>
      <role>Author</role>
    </principalAgent>
    <principalAgent>
      <name>
        <value>Longman Publishers</value>
        <type>PrincipalName</type>
      </name>
      <role>Publisher</role>
    </principalAgent>
    <linkedCreation>
      <name>
        <value>Journal of Science &amp; Technology</value>
        <type>PrincipalTitle</type>
      </name>
      <identifier>
        <nonUriValue>2456-5660</nonUriValue>
        <type>ISSN</type>
      </identifier>
      <referentCreationRole>Part</referentCreationRole>
      <referentCreationSequenceIdentifier>
        <value>8</value>
        <type>VolumeNumber</type>
      </referentCreationSequenceIdentifier>
      <referentCreationSequenceIdentifier>
        <value>12</value>
        <type>IssueNumber</type>
      </referentCreationSequenceIdentifier>
      <referentCreationSequenceIdentifier>
        <value>139-146</value>
        <type>PageNumber</type>
      </referentCreationSequenceIdentifier>
    </linkedCreation>
    <language>en</language>
    <languageOfReferentContent>
      <language>en</language>
      <languageOfReferentContentType>Original</languageOfReferentContentType>
    </languageOfReferentContent>
    <creationDate>
      <date>2023-12-12</date>
      <creationDateType>PublicationDate</creationDateType>
    </creationDate>
  </referentCreation>
</kernelMetadata>

Kernel Metadata Declaration — on demand

✓ Meets every rule of the format

What the DOI identifies

System metadata — ISO 26324:2025, Annex B · DOI Handbook 10.1

The record

The Kernel Metadata Declaration

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