The views of privacy auditors regarding standards and methodologies
Bibliographic record
Abstract
Purpose This paper aims to uncover the practices of different privacy auditors to reveal the extent of any similarities in such practices. The purpose is to investigate the drivers of practices used by privacy auditors and to identify potential for improvements in the practice of privacy auditing so that privacy audits may better serve stakeholders. Design/methodology/approach Six semi-structured interviews with seven privacy auditors and regulators and an analyst across Australia, Canada, New Zealand and the USA are used as the basis for our analysis. Findings The study shows that some privacy auditors view privacy as an organizational issue, which means that all staff within an organization should understand the privacy issues that are relevant to the organization and to its customers. Because this practice goes beyond a mere compliance approach to privacy auditing, it indicates that there is a way to avoid the approach of merely applying standards from national data privacy laws which is an approach that has been subject to criticism because it is not applicable to the current situation of global applications and cross-border data. The interview themes demonstrate that privacy audits face significant challenges, such as the lack of a privacy auditing profession and the difficulty of raising the awareness of organizations and individuals regarding information privacy rights and duties. Originality/value Privacy auditing is mostly unexplored by academic research and little is known about the drivers behind the practice of privacy auditing. This study is the first to document the views of privacy auditors regarding the practices that they use. It also presents novel results regarding the drivers of the practice of privacy auditing and the interests of the beneficiaries of privacy audits. It builds on research that argues for the existence of best practices for privacy (Toy, 2013; Toy and Hay, 2015) and it extends this argument by providing reasons why privacy auditors may benefit from the use of best practices for privacy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.210 | 0.359 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.010 | 0.023 |
| Scholarly communication | 0.021 | 0.012 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".