PreprintCited by 45Digitalization, Law, and RegulationPrivacy, Security, and Data ProtectionLaw, Rights, and Freedoms

Dataveillance Regulation: A Research Framework

Open access
Clarke, Roger, Greenleaf, Graham·2017·SSRN Electronic Journal

Abstract

Dataveillance is the systematic creation and/or use of personal data for the investigation or monitoring of the actions or communications of one or more persons. It emerged in the 1980s, initially as a set of tools for exploiting data that had already been collected for some other purpose. Developments in information technologies, combined with a voracious appetite for social control among government agencies and corporations alike, has seen dataveillance practices diversify and proliferate. The regulatory frameworks that enable and control dataveillance activities appear not to have attracted a great deal of attention in the literature. Data privacy measures, usually referred to as (personal) data protection, have been the subject of a great deal of activity in legislatures, resulting in many countries having data protection oversight agencies and a modest level of jurisprudence. On the other hand, provisions that enable rather than constrain dataveillance are voluminous, both pre-dating data protection laws, and passed subsequently and hence often qualifying or over-ruling them. This paper presents an initial survey and structuring of the field of dataveillance regulation in a manner intended to facilitate the conduct of research in the area. Its scope extends well beyond legislation to encompass all forms of regulatory mechanism. It identifies ways in which the effectiveness or otherwise of regulatory schemes can be evaluated. It suggests a classification scheme that can be applied to dataveillance regulation through technology and by law. This lays, we believe, a foundation for the analysis of the regimes in particular jurisdictions whereby dataveillance practices are regulated, for comparisons among jurisdictions, and for comparative evaluation of degrees of freedom and of authoritarianism.

Integrity signals

Registry

Crossref · 10.2139/ssrn.3073492

Cites

26 of 26 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Cites

Flagged first, then by citations

Authors
Clarke, R., & Greenleaf, G.
Affiliations
  • Australian National University (AU)
  • Macquarie University (AU)
Corresponding author
Greenleaf, Graham
Venue
SSRN Electronic Journal
Publisher
Elsevier BV
Published
2017
Volume
25
ISSN
1556-5068 (ISSN-L)
OpenAlex
W3121509972
Cited by
45
References
26
Citations by year
2026: 1 · 2025: 4 · 2024: 5 · 2023: 5 · 2022: 8 · 2021: 3 · 2020: 16 · 2019: 3
Type
posted content
Language
en
Open access
Locations
  • SSRN Electronic Journal (acceptedVersion)
  • Journal of Law Information & Science
Topics
  • Social Sciences › Law › Digitalization, Law, and Regulation
  • Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
  • Social Sciences › Sociology and Political Science › Law, Rights, and Freedoms
Keywords
dataveillance, surveillance regulation, research framework, privacy law, data protection

About these checks

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.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review