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Cited by 49Digitalization, Law, and RegulationPrivacy, Security, and Data ProtectionDigital Transformation in Law

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Digital Personal Data Protection Act, 2023

Gupta, Nidhi, George, Ammu·2024·Data Governance and the Digital Economy in Asia
DOI 10.4324/9781003505723-3
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Abstract

In the rapidly advancing digital economy era, the imperative for robust personal data protection legislation in India has never been more pressing. The Digital Personal Data Protection (DPDP) Act, 2023 ushers in the country’s new era of data governance. This chapter analyses the Act’s multifaceted provisions and examines the implications on businesses, particularly in the digital sphere. Leaning more towards the “conditional” model of data protection, it is found that the DPDP Act provisions to align with global data protection standards. The positive business implications of the DPDP Act are evident from the lack of data localisation requirements and allowances for unrestricted cross-border data flows.

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Crossref · 10.4324/9781003505723-3

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Metadata

Authors
Gupta, N., & George, A.
ORCID
George, Ammu
Venue
Data Governance and the Digital Economy in Asia
Publisher
Routledge
Published
2024-11-28
Pages
34-53
DOI
10.4324/9781003505723-3
OpenAlex
W4404793717
Cited by
49
References
0
Citations by year
2026: 6 · 2025: 23 · 2024: 19
FWCI
220.72
Type
book chapter
Language
en
Open access
closed
Locations
  • Routledge eBooks (publishedVersion)
  • Research Portal (Queen's University Belfast) (submittedVersion)
Topics
  • Social Sciences › Law › Digitalization, Law, and Regulation
  • Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
  • Social Sciences › Law › Digital Transformation in Law
Keywords
Digital Personal Data Protection Act 2023, personal data protection, data governance, data localization, cross-border data flows, data protection law, India

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.