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What Privacy Evidence is designed to do
Vitor Mattos created Privacy Evidence after an earlier study of privacy and data-protection signals on websites associated with the Igreja Presbiteriana do Brasil (IPB). He describes the software as a research artifact his master’s advisor could inspect. Mattos says the advisor did not ask for this particular software, and the advisor has not validated its results.
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The pipeline starts with website populations provided through a source adapter, preserving where records came from. It then attempts to acquire public web content under a repeatable protocol, detects possible privacy evidence, and sends uncertain cases for human review. Reviewed evidence can be evaluated against versioned profiles for frameworks and requirements such as Brazil’s LGPD, the GDPR, and cookie or ePrivacy rules. The project report describes this as research software, not a legal-compliance product.
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This separation matters: collecting a page, identifying a possible signal, and mapping reviewed evidence to a regulatory profile are distinct stages. A record of those stages lets a researcher examine how an outcome was reached instead of treating a final label as self-explanatory.
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What the October 2026 run found
Mattos reports that the full-population run was executed on October 5, 2026. These are the project’s reported results; the run has not been independently replicated in the cited material.
| Stage or outcome | Project-reported count |
|---|---|
| Records in the IPB source population | 2,993 |
| Records classified as website-eligible | 589 |
| Unique website measurement units after deduplication | 566 |
| Fully measured units | 193 |
| Partially measured units | 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. Recording these outcomes is analytically important: an unavailable website is not the same observation as a successfully inspected website with no detected signal.
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Mattos also reports averages of 27.2% LGPD-oriented evidence coverage, 5.3% full observed support, and 6.6% any observed support. These are protocol-specific measures of evidence observed by the project, not legal compliance rates. They should not be read as the percentage of organizations that comply, nor compared with the 2025 monograph as a change over time: the populations, definitions, and measurement protocols differ, and the report says missing entity-level 2025 artifacts prevent reliable reconstruction of all record transitions.
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Why provenance is the main methodological change
In Mattos’s account, the 2025 workflow relied on scripts, SQLite, URL checking, manual cleaning, and page-by-page review. It did not preserve every source snapshot, intermediate database state, and human decision needed to reconstruct the workflow precisely later.
The newer workflow is designed to retain a more complete path through the analysis, including:
- Source classification, eligibility decisions, and duplicate handling.
- Acquisition failures and explicit missing-data states.
- Acquired evidence, detector versions, and protocol, schema, and profile settings.
- Human-review status and decisions for ambiguous cases.
That provenance improves inspectability and makes reruns easier to interpret. It does not, by itself, establish that every detection is correct or that a result is representative of all privacy practices at an organization.
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What a website measurement can establish
The study object is publicly observable privacy evidence exposed by digital resources. A website is only one observable slice of a broader privacy program. A missing signal on a crawled page therefore does not prove that an organization lacks a relevant practice, and it does not establish non-compliance. Conversely, the presence of a policy or notice is not by itself proof that the organization fulfills every legal obligation.
As Mattos puts it, “Privacy Evidence deliberately does not say that a website or organization is legally compliant.” Its findings are bounded by what the protocol could access and classify. Technical access conditions, page selection, detector behavior, and reviewer interpretation all shape what can be observed.
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Why machine suggestions still need validation
The project includes machine-learning suggestions, but Mattos characterizes that component as experimental. False positives and false negatives remain possible, and acquisition conditions can affect what the detector sees. Stronger claims about classification performance require independent human annotation and detector evaluation. Human review is therefore not decorative: it is a way to keep uncertain machine-generated classifications open to inspection rather than silently turning them into facts.
Making sensitive-data research reproducible
Reproducibility need not mean publishing sensitive original records. Shepherd and colleagues distinguish reproducing quantitative results using original data and analysis code from replication using a different dataset. For sensitive data that cannot be released, they recommend publicly sharing analysis code, simulated data, and the results of running the code on that simulated data. Simulated data can demonstrate how an analysis works, but it is not equivalent to the original research data.
That approach fits research software such as Privacy Evidence: transparent code and simulated-data analyses can make methods reviewable while respecting limits on disclosure. A 2018 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 figures describe the surveyed analysts and the late-2017 survey period, not researchers across all fields or current practice.
What the project does—and does not—demonstrate
The project report’s most defensible result is methodological: it accounts for the path from 2,993 source records to the final measurement state, including deduplication, partial measurement, and non-measurability. The reported evidence percentages are outputs of that workflow, not proof of improved or worsened legal compliance relative to an earlier study.
Mattos summarizes the emphasis this way: “The most relevant outcome was not a single percentage. It was the ability to reproduce the complete path from all 2,993 IPB source records to the final analytical state.” For researchers, that is a useful standard to aim for: preserve not just the final dataset, but also the decisions and failures that explain how it came to exist.
Quick Recap
Sources
- Vitor Mattos, “Privacy Evidence: A Reproducible Web Measurement Pipeline for Empirical Privacy Research”, project report, October 2026.
- J. K. Harris et al., “Use of reproducible research practices in public health: A survey of public health analysts”, PLOS ONE, September 12, 2018.
- Bryan E. Shepherd et al., “A Pragmatic Approach for Reproducible Research With Sensitive Data”, American Journal of Epidemiology, August 2, 2017.
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