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Privacy Evidence turns a website privacy study into a traceable research workflow: it records which sites entered the study, what could be measured, what evidence was found, and where human review was needed. Its October 2026 report describes a more reproducible path through the data—not proof that the organizations studied were compliant or non-compliant with privacy law.
What Privacy Evidence is designed to measure
Privacy Evidence is an open-source research instrument created by Vitor Mattos after an earlier study of privacy and data-protection signals on websites associated with the Igreja Presbiteriana do Brasil (IPB). Its subject is publicly observable privacy evidence exposed by digital resources, not an organization’s full privacy program.
The workflow begins with a population supplied by a source adapter. It retains information about that source, acquires public web content under a repeatable protocol, detects possible privacy evidence, and sends uncertain cases for human review. Reviewed evidence can then be assessed against versioned profiles such as LGPD, GDPR, and cookie/ePrivacy requirements. The acquisition and detection stages are distinct from the later regulatory mapping.
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Mattos describes the tool as research software, not a legal-compliance product. As he puts it, “Privacy Evidence deliberately does not say that a website or organization is legally compliant.” A missing signal on a crawled page does not establish that an organization lacks the related practice: the crawl observes only a limited public slice, and the result also depends on what the acquisition protocol could reach.
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What the October 2026 run reported
Mattos’s project report says the full-population run took place on October 5, 2026. These are author-reported results; the run has not been independently replicated in the sources cited here.
| Stage or measure | Reported result |
|---|---|
| Records in the source population | 2,993 |
| Records classified as website-eligible | 589 |
| Unique website measurement units after deduplication | 566 |
| Units fully measured | 193 |
| Units partially measured | 12 |
| Units not measurable under the protocol | 361 |
The report says non-measurable outcomes included DNS or TLS failures, anti-bot behavior, rate limits, timeouts, redirects, and crawl-budget limits. Treating these as explicit outcomes matters: an unavailable observation is not the same as evidence that a privacy practice is absent.
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The report also gives three averages for the run: 27.2% LGPD-oriented evidence coverage, 5.3% full observed support, and 6.6% any observed support. These are measures defined by the project’s protocol, not legal compliance rates. They should not be read as a verdict about the organizations, nor compared directly with counts from the earlier 2025 monograph as a compliance trend. The populations, definitions, and measurement protocols differ, and Mattos says missing entity-level 2025 artifacts prevent reliable reconstruction of every record transition.
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Why the workflow is more reproducible
The key change is not a higher or lower privacy score. It is a fuller record of how the analytical state was reached. Mattos describes the 2025 workflow as a combination of scripts, SQLite, URL checking, manual cleaning, and page-by-page review. In his account, it did not preserve every source snapshot, intermediate database state, and human decision needed for precise reconstruction later.
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The newer workflow records the steps that can otherwise disappear between collection and analysis:
- Population and eligibility: source classification and eligibility decisions remain visible.
- Duplicates: duplicate handling is documented so record counts can be traced to unique measurement units.
- Acquisition outcomes: failures and missing-data states are retained instead of being silently dropped.
- Evidence and versions: acquired evidence is kept immutable, alongside detector versions and protocol, schema, and profile settings.
- Human decisions: review state is recorded, making it possible to inspect where automated suggestions were accepted, rejected, or left uncertain.
That provenance trail lets another researcher ask not only “What was the final result?” but also “Which records were included, what could the system reach, which evidence supported a classification, and what changed during review?” Mattos identifies the ability to reproduce the path from all 2,993 source records to the final analytical state as the project’s most relevant outcome.
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Where automation and legal mapping need caution
Automated detection can help find candidate evidence at scale, but the report identifies false positives, false negatives, and acquisition effects as limitations. The machine-learning component is experimental. Mattos says stronger claims about its classification performance require independent human annotation and detector evaluation.
Human review is therefore part of the method, not a decorative final check. It provides a way to inspect ambiguous suggestions and preserve decisions, while versioned regulatory profiles make the mapping rules more explicit. Neither feature by itself establishes that a detector is accurate or that a website meets a legal obligation. A finding depends on the available pages, the protocol, the detector, the reviewer, and the selected profile.
How to share reproducible work when original data is sensitive
Reproducibility does not always mean publishing the original records. Shepherd and colleagues distinguish reproducing quantitative results from original data and code from replication using a different dataset. For work whose original data cannot be released, they recommend publicly sharing analysis code, simulated data, and the results produced when that code is run on the simulated data. The simulated data supports inspection of the workflow; it is not a substitute for the original research data.
This approach is relevant to privacy research, where disclosure constraints can limit what is safe to publish. A 2017 survey of public-health analysts by Harris and colleagues found that 14.4% had shared code, data, or both; 49.8% identified data privacy as a barrier to reproducible practices, and 41.7% identified lack of time. Those percentages describe the survey population and its late-2017 field period, not researchers in every discipline today.
Privacy Evidence’s broader lesson is that reproducibility can be improved by publishing a transparent workflow and suitable simulated analyses while protecting sensitive source material. The choices still need to fit the study’s data and disclosure risks.
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Mattos says he built Privacy Evidence as a research artifact his master’s advisor could inspect. He also says the advisor did not request this exact software and has not validated its results. The project is therefore best understood as an attempt to make a prior study’s workflow more inspectable, not as advisor-certified software or an independently confirmed measurement of compliance.
Sources: Vitor Mattos, project report, October 2026; Harris et al., PLOS ONE, September 12, 2018; Shepherd et al., American Journal of Epidemiology, August 2, 2017.
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