PrivComp-KG : Leveraging Knowledge Graph and Large Language Models for Privacy Policy Compliance Verification Article Swipe
Leon Garza
,
Lavanya Elluri
,
Anantaa Kotal
,
Aritran Piplai
,
Deepti Gupta
,
Anupam Joshi
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2404.19744
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2404.19744
Data protection and privacy is becoming increasingly crucial in the digital era. Numerous companies depend on third-party vendors and service providers to carry out critical functions within their operations, encompassing tasks such as data handling and storage. However, this reliance introduces potential vulnerabilities, as these vendors' security measures and practices may not always align with the standards expected by regulatory bodies. Businesses are required, often under the penalty of law, to ensure compliance with the evolving regulatory rules. Interpreting and implementing these regulations pose challenges due to their complexity. Regulatory documents are extensive, demanding significant effort for interpretation, while vendor-drafted privacy policies often lack the detail required for full legal compliance, leading to ambiguity. To ensure a concise interpretation of the regulatory requirements and compliance of organizational privacy policy with said regulations, we propose a Large Language Model (LLM) and Semantic Web based approach for privacy compliance. In this paper, we develop the novel Privacy Policy Compliance Verification Knowledge Graph, PrivComp-KG. It is designed to efficiently store and retrieve comprehensive information concerning privacy policies, regulatory frameworks, and domain-specific knowledge pertaining to the legal landscape of privacy. Using Retrieval Augmented Generation, we identify the relevant sections in a privacy policy with corresponding regulatory rules. This information about individual privacy policies is populated into the PrivComp-KG. Combining this with the domain context and rules, the PrivComp-KG can be queried to check for compliance with privacy policies by each vendor against relevant policy regulations. We demonstrate the relevance of the PrivComp-KG, by verifying compliance of privacy policy documents for various organizations.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2404.19744
- https://arxiv.org/pdf/2404.19744
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4396600952
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4396600952Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2404.19744Digital Object Identifier
- Title
-
PrivComp-KG : Leveraging Knowledge Graph and Large Language Models for Privacy Policy Compliance VerificationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-04-30Full publication date if available
- Authors
-
Leon Garza, Lavanya Elluri, Anantaa Kotal, Aritran Piplai, Deepti Gupta, Anupam JoshiList of authors in order
- Landing page
-
https://arxiv.org/abs/2404.19744Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2404.19744Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2404.19744Direct OA link when available
- Concepts
-
Compliance (psychology), Computer science, Graph, Privacy policy, Computer security, Information privacy, Theoretical computer science, Psychology, Social psychologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4396600952 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2404.19744 |
| ids.doi | https://doi.org/10.48550/arxiv.2404.19744 |
| ids.openalex | https://openalex.org/W4396600952 |
| fwci | |
| type | preprint |
| title | PrivComp-KG : Leveraging Knowledge Graph and Large Language Models for Privacy Policy Compliance Verification |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T11045 |
| topics[0].field.id | https://openalex.org/fields/33 |
| topics[0].field.display_name | Social Sciences |
| topics[0].score | 0.9775000214576721 |
| topics[0].domain.id | https://openalex.org/domains/2 |
| topics[0].domain.display_name | Social Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/3312 |
| topics[0].subfield.display_name | Sociology and Political Science |
| topics[0].display_name | Privacy, Security, and Data Protection |
| topics[1].id | https://openalex.org/T11719 |
| topics[1].field.id | https://openalex.org/fields/18 |
| topics[1].field.display_name | Decision Sciences |
| topics[1].score | 0.9164000153541565 |
| topics[1].domain.id | https://openalex.org/domains/2 |
| topics[1].domain.display_name | Social Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1803 |
| topics[1].subfield.display_name | Management Science and Operations Research |
| topics[1].display_name | Data Quality and Management |
| topics[2].id | https://openalex.org/T10927 |
| topics[2].field.id | https://openalex.org/fields/33 |
| topics[2].field.display_name | Social Sciences |
| topics[2].score | 0.9157999753952026 |
| topics[2].domain.id | https://openalex.org/domains/2 |
| topics[2].domain.display_name | Social Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/3312 |
| topics[2].subfield.display_name | Sociology and Political Science |
| topics[2].display_name | Access Control and Trust |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C2781460075 |
| concepts[0].level | 2 |
| concepts[0].score | 0.6941380500793457 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q1399332 |
| concepts[0].display_name | Compliance (psychology) |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.6805871725082397 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C132525143 |
| concepts[2].level | 2 |
| concepts[2].score | 0.4800335466861725 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q141488 |
| concepts[2].display_name | Graph |
| concepts[3].id | https://openalex.org/C102938260 |
| concepts[3].level | 3 |
| concepts[3].score | 0.4664784073829651 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q1999831 |
| concepts[3].display_name | Privacy policy |
| concepts[4].id | https://openalex.org/C38652104 |
| concepts[4].level | 1 |
| concepts[4].score | 0.3304087519645691 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q3510521 |
| concepts[4].display_name | Computer security |
| concepts[5].id | https://openalex.org/C123201435 |
| concepts[5].level | 2 |
| concepts[5].score | 0.29687100648880005 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q456632 |
| concepts[5].display_name | Information privacy |
| concepts[6].id | https://openalex.org/C80444323 |
| concepts[6].level | 1 |
| concepts[6].score | 0.27992478013038635 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q2878974 |
| concepts[6].display_name | Theoretical computer science |
| concepts[7].id | https://openalex.org/C15744967 |
| concepts[7].level | 0 |
| concepts[7].score | 0.10785803198814392 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q9418 |
| concepts[7].display_name | Psychology |
| concepts[8].id | https://openalex.org/C77805123 |
| concepts[8].level | 1 |
| concepts[8].score | 0.0 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q161272 |
| concepts[8].display_name | Social psychology |
| keywords[0].id | https://openalex.org/keywords/compliance |
| keywords[0].score | 0.6941380500793457 |
| keywords[0].display_name | Compliance (psychology) |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.6805871725082397 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/graph |
| keywords[2].score | 0.4800335466861725 |
| keywords[2].display_name | Graph |
| keywords[3].id | https://openalex.org/keywords/privacy-policy |
| keywords[3].score | 0.4664784073829651 |
| keywords[3].display_name | Privacy policy |
| keywords[4].id | https://openalex.org/keywords/computer-security |
| keywords[4].score | 0.3304087519645691 |
| keywords[4].display_name | Computer security |
| keywords[5].id | https://openalex.org/keywords/information-privacy |
| keywords[5].score | 0.29687100648880005 |
| keywords[5].display_name | Information privacy |
| keywords[6].id | https://openalex.org/keywords/theoretical-computer-science |
| keywords[6].score | 0.27992478013038635 |
| keywords[6].display_name | Theoretical computer science |
| keywords[7].id | https://openalex.org/keywords/psychology |
| keywords[7].score | 0.10785803198814392 |
| keywords[7].display_name | Psychology |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:2404.19744 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400194 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | arXiv (Cornell University) |
| locations[0].source.host_organization | https://openalex.org/I205783295 |
| locations[0].source.host_organization_name | Cornell University |
| locations[0].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[0].license | |
| locations[0].pdf_url | https://arxiv.org/pdf/2404.19744 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | text |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | http://arxiv.org/abs/2404.19744 |
| locations[1].id | doi:10.48550/arxiv.2404.19744 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400194 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | arXiv (Cornell University) |
| locations[1].source.host_organization | https://openalex.org/I205783295 |
| locations[1].source.host_organization_name | Cornell University |
| locations[1].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | |
| locations[1].raw_type | article |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | https://doi.org/10.48550/arxiv.2404.19744 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5096147983 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Leon Garza |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Garza, Leon |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5047534464 |
| authorships[1].author.orcid | |
| authorships[1].author.display_name | Lavanya Elluri |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Elluri, Lavanya |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5062851464 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-1818-9705 |
| authorships[2].author.display_name | Anantaa Kotal |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Kotal, Anantaa |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5014855298 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-6437-1324 |
| authorships[3].author.display_name | Aritran Piplai |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Piplai, Aritran |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5001893144 |
| authorships[4].author.orcid | https://orcid.org/0000-0001-7844-9092 |
| authorships[4].author.display_name | Deepti Gupta |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Gupta, Deepti |
| authorships[4].is_corresponding | False |
| authorships[5].author.id | https://openalex.org/A5020975010 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-8641-3193 |
| authorships[5].author.display_name | Anupam Joshi |
| authorships[5].author_position | last |
| authorships[5].raw_author_name | Joshi, Anupam |
| authorships[5].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/2404.19744 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | PrivComp-KG : Leveraging Knowledge Graph and Large Language Models for Privacy Policy Compliance Verification |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T11045 |
| primary_topic.field.id | https://openalex.org/fields/33 |
| primary_topic.field.display_name | Social Sciences |
| primary_topic.score | 0.9775000214576721 |
| primary_topic.domain.id | https://openalex.org/domains/2 |
| primary_topic.domain.display_name | Social Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/3312 |
| primary_topic.subfield.display_name | Sociology and Political Science |
| primary_topic.display_name | Privacy, Security, and Data Protection |
| related_works | https://openalex.org/W4391375266, https://openalex.org/W2748952813, https://openalex.org/W2410395228, https://openalex.org/W2390279801, https://openalex.org/W3125941065, https://openalex.org/W2358668433, https://openalex.org/W2484615095, https://openalex.org/W4366449942, https://openalex.org/W2376932109, https://openalex.org/W1521974481 |
| cited_by_count | 1 |
| counts_by_year[0].year | 2024 |
| counts_by_year[0].cited_by_count | 1 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:2404.19744 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://arxiv.org/pdf/2404.19744 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | text |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/2404.19744 |
| primary_location.id | pmh:oai:arXiv.org:2404.19744 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400194 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | arXiv (Cornell University) |
| primary_location.source.host_organization | https://openalex.org/I205783295 |
| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | |
| primary_location.pdf_url | https://arxiv.org/pdf/2404.19744 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | text |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/2404.19744 |
| publication_date | 2024-04-30 |
| publication_year | 2024 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 116, 134, 196 |
| abstract_inverted_index.In | 147 |
| abstract_inverted_index.It | 161 |
| abstract_inverted_index.To | 114 |
| abstract_inverted_index.We | 241 |
| abstract_inverted_index.as | 32, 43 |
| abstract_inverted_index.be | 225 |
| abstract_inverted_index.by | 58, 234, 248 |
| abstract_inverted_index.in | 8, 195 |
| abstract_inverted_index.is | 4, 162, 209 |
| abstract_inverted_index.of | 68, 119, 125, 184, 245, 251 |
| abstract_inverted_index.on | 15 |
| abstract_inverted_index.to | 21, 70, 86, 112, 164, 180, 227 |
| abstract_inverted_index.we | 132, 150, 190 |
| abstract_inverted_index.Web | 141 |
| abstract_inverted_index.and | 2, 18, 35, 48, 79, 123, 139, 167, 176, 220 |
| abstract_inverted_index.are | 62, 91 |
| abstract_inverted_index.can | 224 |
| abstract_inverted_index.due | 85 |
| abstract_inverted_index.for | 96, 107, 144, 229, 255 |
| abstract_inverted_index.may | 50 |
| abstract_inverted_index.not | 51 |
| abstract_inverted_index.out | 23 |
| abstract_inverted_index.the | 9, 55, 66, 74, 104, 120, 152, 181, 192, 212, 217, 222, 243, 246 |
| abstract_inverted_index.Data | 0 |
| abstract_inverted_index.This | 203 |
| abstract_inverted_index.data | 33 |
| abstract_inverted_index.each | 235 |
| abstract_inverted_index.era. | 11 |
| abstract_inverted_index.full | 108 |
| abstract_inverted_index.into | 211 |
| abstract_inverted_index.lack | 103 |
| abstract_inverted_index.law, | 69 |
| abstract_inverted_index.pose | 83 |
| abstract_inverted_index.said | 130 |
| abstract_inverted_index.such | 31 |
| abstract_inverted_index.this | 38, 148, 215 |
| abstract_inverted_index.with | 54, 73, 129, 199, 216, 231 |
| abstract_inverted_index.(LLM) | 138 |
| abstract_inverted_index.Large | 135 |
| abstract_inverted_index.Model | 137 |
| abstract_inverted_index.Using | 186 |
| abstract_inverted_index.about | 205 |
| abstract_inverted_index.align | 53 |
| abstract_inverted_index.based | 142 |
| abstract_inverted_index.carry | 22 |
| abstract_inverted_index.check | 228 |
| abstract_inverted_index.legal | 109, 182 |
| abstract_inverted_index.novel | 153 |
| abstract_inverted_index.often | 64, 102 |
| abstract_inverted_index.store | 166 |
| abstract_inverted_index.tasks | 30 |
| abstract_inverted_index.their | 27, 87 |
| abstract_inverted_index.these | 44, 81 |
| abstract_inverted_index.under | 65 |
| abstract_inverted_index.while | 98 |
| abstract_inverted_index.Graph, | 159 |
| abstract_inverted_index.Policy | 155 |
| abstract_inverted_index.always | 52 |
| abstract_inverted_index.depend | 14 |
| abstract_inverted_index.detail | 105 |
| abstract_inverted_index.domain | 218 |
| abstract_inverted_index.effort | 95 |
| abstract_inverted_index.ensure | 71, 115 |
| abstract_inverted_index.paper, | 149 |
| abstract_inverted_index.policy | 128, 198, 239, 253 |
| abstract_inverted_index.rules, | 221 |
| abstract_inverted_index.rules. | 77, 202 |
| abstract_inverted_index.vendor | 236 |
| abstract_inverted_index.within | 26 |
| abstract_inverted_index.Privacy | 154 |
| abstract_inverted_index.against | 237 |
| abstract_inverted_index.bodies. | 60 |
| abstract_inverted_index.concise | 117 |
| abstract_inverted_index.context | 219 |
| abstract_inverted_index.crucial | 7 |
| abstract_inverted_index.develop | 151 |
| abstract_inverted_index.digital | 10 |
| abstract_inverted_index.leading | 111 |
| abstract_inverted_index.penalty | 67 |
| abstract_inverted_index.privacy | 3, 100, 127, 145, 172, 197, 207, 232, 252 |
| abstract_inverted_index.propose | 133 |
| abstract_inverted_index.queried | 226 |
| abstract_inverted_index.service | 19 |
| abstract_inverted_index.various | 256 |
| abstract_inverted_index.vendors | 17 |
| abstract_inverted_index.However, | 37 |
| abstract_inverted_index.Language | 136 |
| abstract_inverted_index.Numerous | 12 |
| abstract_inverted_index.Semantic | 140 |
| abstract_inverted_index.approach | 143 |
| abstract_inverted_index.becoming | 5 |
| abstract_inverted_index.critical | 24 |
| abstract_inverted_index.designed | 163 |
| abstract_inverted_index.evolving | 75 |
| abstract_inverted_index.expected | 57 |
| abstract_inverted_index.handling | 34 |
| abstract_inverted_index.identify | 191 |
| abstract_inverted_index.measures | 47 |
| abstract_inverted_index.policies | 101, 208, 233 |
| abstract_inverted_index.privacy. | 185 |
| abstract_inverted_index.relevant | 193, 238 |
| abstract_inverted_index.reliance | 39 |
| abstract_inverted_index.required | 106 |
| abstract_inverted_index.retrieve | 168 |
| abstract_inverted_index.sections | 194 |
| abstract_inverted_index.security | 46 |
| abstract_inverted_index.storage. | 36 |
| abstract_inverted_index.vendors' | 45 |
| abstract_inverted_index.Augmented | 188 |
| abstract_inverted_index.Combining | 214 |
| abstract_inverted_index.Knowledge | 158 |
| abstract_inverted_index.Retrieval | 187 |
| abstract_inverted_index.companies | 13 |
| abstract_inverted_index.demanding | 93 |
| abstract_inverted_index.documents | 90, 254 |
| abstract_inverted_index.functions | 25 |
| abstract_inverted_index.knowledge | 178 |
| abstract_inverted_index.landscape | 183 |
| abstract_inverted_index.policies, | 173 |
| abstract_inverted_index.populated | 210 |
| abstract_inverted_index.potential | 41 |
| abstract_inverted_index.practices | 49 |
| abstract_inverted_index.providers | 20 |
| abstract_inverted_index.relevance | 244 |
| abstract_inverted_index.required, | 63 |
| abstract_inverted_index.standards | 56 |
| abstract_inverted_index.verifying | 249 |
| abstract_inverted_index.Businesses | 61 |
| abstract_inverted_index.Compliance | 156 |
| abstract_inverted_index.Regulatory | 89 |
| abstract_inverted_index.ambiguity. | 113 |
| abstract_inverted_index.challenges | 84 |
| abstract_inverted_index.compliance | 72, 124, 230, 250 |
| abstract_inverted_index.concerning | 171 |
| abstract_inverted_index.extensive, | 92 |
| abstract_inverted_index.individual | 206 |
| abstract_inverted_index.introduces | 40 |
| abstract_inverted_index.pertaining | 179 |
| abstract_inverted_index.protection | 1 |
| abstract_inverted_index.regulatory | 59, 76, 121, 174, 201 |
| abstract_inverted_index.Generation, | 189 |
| abstract_inverted_index.PrivComp-KG | 223 |
| abstract_inverted_index.complexity. | 88 |
| abstract_inverted_index.compliance, | 110 |
| abstract_inverted_index.compliance. | 146 |
| abstract_inverted_index.demonstrate | 242 |
| abstract_inverted_index.efficiently | 165 |
| abstract_inverted_index.frameworks, | 175 |
| abstract_inverted_index.information | 170, 204 |
| abstract_inverted_index.operations, | 28 |
| abstract_inverted_index.regulations | 82 |
| abstract_inverted_index.significant | 94 |
| abstract_inverted_index.third-party | 16 |
| abstract_inverted_index.Interpreting | 78 |
| abstract_inverted_index.PrivComp-KG, | 247 |
| abstract_inverted_index.PrivComp-KG. | 160, 213 |
| abstract_inverted_index.Verification | 157 |
| abstract_inverted_index.encompassing | 29 |
| abstract_inverted_index.implementing | 80 |
| abstract_inverted_index.increasingly | 6 |
| abstract_inverted_index.regulations, | 131 |
| abstract_inverted_index.regulations. | 240 |
| abstract_inverted_index.requirements | 122 |
| abstract_inverted_index.comprehensive | 169 |
| abstract_inverted_index.corresponding | 200 |
| abstract_inverted_index.interpretation | 118 |
| abstract_inverted_index.organizational | 126 |
| abstract_inverted_index.organizations. | 257 |
| abstract_inverted_index.vendor-drafted | 99 |
| abstract_inverted_index.domain-specific | 177 |
| abstract_inverted_index.interpretation, | 97 |
| abstract_inverted_index.vulnerabilities, | 42 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile |