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REVIEW 5 major objections 4 minor 69 references

From "I have nothing to hide" to "It looks like stalking": Measuring Americans' Level of Comfort with Individual Mobility Features Extracted from Location Data

T0 review · 5 major / 4 minor · reviewed 2026-08-08 · deepseek-v4-flash

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Pith's one-line read This paper claims that Americans' comfort with sharing location data is determined more by what is extracted—detailed trip routes versus obfuscated census-tract summaries—than by whether tracking happens at all, and that obfuscation…

desk verdict Real new measurement of trajectory-feature privacy perceptions, but the headline effects depend on an unreported post hoc exclusion step; deserves review with a revision request. read the letter →

arxiv 2502.05686 v1 pith:BZWZPEY6 submitted 2025-02-08 cs.CY

Naman Awasthi , Saad Mohammad Abrar , Daniel Smolyak , Vanessa Frias-Martinez This is my paper · ORCID
classification cs.CY
keywords locationdataprivacybrokersobfuscationtrajectoryperceptionsfactorialvignettesurveymixed-effectsordinalregressioncomfortprediction
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper claims that Americans' comfort with sharing location data depends mainly on the specific feature being extracted—detailed trip routes and walking paths feel intrusive, while obfuscated versions of the same features feel acceptable—not just on whether tracking happens at all. The claim comes from a factorial vignette survey in which 1,405 U.S. participants rated 7,025 randomized scenarios pairing nine actors, ten purposes, and eighteen location features, with each feature shown in a detailed and an obfuscated form. The results show trajectory features are associated with the strongest discomfort, that obfuscation to census-tract or county level raises comfort substantially, and that Hispanic respondents report higher comfort than White respondents. If the findings hold, they give data brokers and regulators a user-grounded basis for deciding which location features should require explicit consent and which privacy-preserving transformations actually reassure people.

What carries the argument

The paper's machinery is the factorial vignette: each survey item presents a fixed template—'Actor X wants to do Purpose Y, and for that they need Feature Z'—with one of nine actors, ten purposes, and eighteen location features drawn from the plausible combinations (445 total). Each feature comes in a 'detailed' version (GPS points, exact routes, pin-point home) and an 'obfuscated' version (census tract or county, charts without routes, straight lines between tract centroids), and every vignette is accompanied by an interactive map or chart so respondents see exactly what the feature would look like. Comfort is measured on a 5-point Likert scale and modeled with a mixed-effects ordinal logistic regression in which participant ID and vignette identity are random effects; interaction effects between features and actors or purposes are then probed with Kruskal-Wallis and Dunn post-hoc tests, and the same data feed supervised classifiers for predicting comfort from context, demographics, and privacy attitudes.

What would settle it

Have a fresh sample of participants view the same vignette either with its interactive visualization or with a text-only description of the feature, and compare comfort ratings; if the visualization changes ratings substantially, the measured 'feature' effects are partly artifacts of the graphic. A second check: ask participants after the vignette what the map actually showed (e.g., that the polygon is their home county, that the straight line is not the real route); if a large share misidentifies these elements, the comfort levels do not correspond to the intended features.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is a quantitative map of U.S. privacy comfort across the specific location features that data brokers actually sell. Using a vignette survey in which each question paired one actor, one purpose, and one location feature, the authors show that trajectory-based features are consistently associated with lower comfort than visits to points of interest: the odds of being comfortable with detailed most-frequent-trips are 0.74 times the odds for detailed places visited (p<0.05). Obfuscation reverses this: obfuscated frequent walking activity (odds ratio 1.58), obfuscated home location (1.55), and obfuscated work location (1.46) are each significantly more comfortable than the detailed-place-visits baseline. Race also matters: Hispanic respondents have 1.65 times the odds of White respondents of rating a scenario comfortable, while no significant overall education effect appears in the main regression. Finally, a supervised model that adds privacy attitude answers to the actor-purpose-feature-demographic tuple reaches an F1 score of 0.60 for binary comfortable-versus-uncomfortable prediction.

Load-bearing premise

The load-bearing premise is that the synthetic map and chart visualizations shown in the vignettes convey to each of the 1,405 participants the same concrete feature they would be asked to share—an assumption validated only by a qualitative pilot with five respondents before the main fielding.

Editorial extensions

If this is right

  • Data brokers and app SDKs could offer per-feature consent screens organized by the six feature clusters, letting users opt into detailed versus obfuscated versions rather than a single all-or-nothing location permission.
  • Regulators writing bright-line rules for location data could reasonably treat detailed trajectory features (frequent trips, walking routes) as higher-risk than place-visit categories, and treat obfuscation to census-tract level as a meaningful mitigation.
  • Because obfuscated walking activity and home or work locations reach comfort levels at or above detailed place visits, a default-obfuscated data pipeline with an opt-in for detail would align data broker practice with measured user preferences.
  • Comfort prediction that reaches F1 0.60 using actor-purpose-feature plus demographics and privacy attitudes is accurate enough to let companies pre-test a new location feature's acceptability before deployment, though not precise enough to replace consent.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The paper's feature set does not include health-care, religious, or protest destinations as explicit trip types, so the measured comfort for detailed trajectories probably overstates comfort for the most sensitive destination categories; a vignette set that adds these destinations would test whether the 'stalking' reaction intensifies.
  • Obfuscation comfort may signal perceived rather than actual anonymity: a census-tract polygon still reveals the home neighborhood, and a straight line between tract centroids still reveals commute direction; a follow-up that asks respondents what an adversary could infer from the obfuscated map could separate perceived from actual privacy.
  • The predictive jump from F1 around 0.28 to 0.60 when privacy attitudes are added suggests a short attitudes questionnaire, not demographics, is the scalable predictor of location-sharing comfort; the paper does not propose such an instrument, but the data support building one.
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Signed reviews

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 4 minor

Summary. The paper reports a factorial vignette survey of 1,405 U.S. participants, each answering five randomized vignettes that combine actors, purposes, and location features, with features presented in detailed or obfuscated forms. The authors fit a weighted mixed-effects ordinal regression (Table 5) to estimate effects on a 5-point comfort scale, use Kruskal-Wallis and Dunn post-hoc tests for interaction analyses, and train classifiers to predict comfort from vignette and demographic or privacy-attitude features. The headline findings are that detailed trajectory features reduce comfort relative to detailed points of interest, some obfuscation practices increase comfort, and race/ethnicity and education are associated with differences in comfort; the abstract also reports a predictive F1 score of 0.6.

Significance. If the central empirical findings hold, the paper makes a useful contribution to the contextual-integrity literature on location privacy by extending it from points of interest to trajectory-derived features and to current data-broker obfuscation practices. The factorial design is appropriate for the research questions, and the use of a mixed-effects ordinal model with random intercepts for participant and vignette is a sound analytic choice, supported by assumption checks and model comparison in the appendix. The paper also provides a large set of interaction and subgroup analyses that are transparently reported in the appendix. The main risk is not the analytic framework but the quality and comparability of the analytic sample after the exclusions described in Section 5, plus a few claims that outrun the reported numbers.

major comments (5)
  1. [Section 7 and Table 14] The response-exclusion procedure is not quantified and is outcome-dependent. The text reports that 7,025 vignette responses were reduced by removing answers whose free-text explanations "did not clearly match" the selected Likert comfort level, judged by two researchers who "agreed upon its quality," plus responses faster than 10 seconds. No exclusion counts, explicit coding criteria, or inter-rater reliability are given, and the analytic sample size for Table 5 is never stated. Because whether a response is kept depends on the written justification given for the same comfort rating that is the dependent variable, differential exclusion across features, actors, purposes, or demographic groups could produce or amplify the headline effects in Table 5, such as Most Frequent Trips (Detailed) OR 0.74 and Frequent Walking Activity (Obfuscated) OR 1.58. Please report how many responses were removed by each criterion, by feature/actor/purpose and by demographic stratum, and re-estimate the main regressions and RQ2 mean comparisons with and without these exclusions as a robustness check.
  2. [Section 8 and Table 5] Section 7 states that "all means were higher for obfuscation features than for their detailed counterpart," but Table 14 contradicts this: International visits (Detailed) has mean 0.286 versus 0.247 for International visits (Obfuscated), and Work location (Detailed) has mean 0.268 versus 0.263 for Work location (Obfuscated). This "all" claim is load-bearing for RQ2, which the abstract summarizes as "some data broker based obfuscation practices increase levels of comfort." Please revise the claim to the subset of features for which the data actually show higher means, and correct the related sentence in Section 10 ("the mean values were lower") if it was intended to say "higher."
  3. [Section 9 and Tables 7-8] The abstract and conclusion claim that education has an effect on data-sharing privacy perceptions, but Table 5 shows no statistically significant education coefficient (Bachelors and above OR 0.814, p=0.103; Under Highschool OR 0.983, p=0.94). The only supporting evidence is the exploratory interaction analysis in Section 8.2 and Table 22. The claim should be limited to the interaction results and the conclusion reformulated accordingly, or the main-effects regression should be complemented by a preregistered or otherwise explicitly confirmatory interaction test. As written, the abstract overstates what the primary model supports.
  4. [Section 3] The predictive modeling section does not describe how the 80/20 train/test split handles the repeated-measures structure of the data. Each participant contributes five vignette responses, and Model 2 includes individual privacy attitudes as features; a random split can place the same participant's other responses in the training set, leaking individual-level information and inflating the reported F1 of 0.6. Please use participant-grouped cross-validation (for example, GroupKFold or leave-participants-out) and report class frequencies and per-participant performance, or explicitly justify why a response-level split is appropriate.
  5. [Section 5] The validity of every comfort rating depends on participants' comprehension of the synthetic visualizations, but the only reported validation is a qualitative study with 5 Craigslist respondents who each answered 10 vignette questions. That is weak evidence for comprehension across 1,405 participants from a broader U.S. panel, especially because the detailed and obfuscated visualizations differ substantially in visual complexity. Please add a quantitative comprehension check or report coding of the free-text explanations by feature type to show that non-comprehension does not vary systematically across features and obfuscation conditions; otherwise the feature comparisons in Table 5 may partly reflect reactions to visual complexity rather than to the intended location feature.
minor comments (4)
  1. [Section 8.1] The justification for the 10-second cutoff cites an average adult reading speed of "100-1200 words/min," which appears to be a typo; 1200 words per minute is far above typical reading speeds. Please correct the range and show the response-time distribution used to motivate the cutoff.
  2. [Table 1 and Table 5] There is an unresolved cross-reference in Section 8.1 to "Tables ?? in the Appendix"; please replace it with the actual table numbers for the race/ethnicity interaction analyses.
  3. [Section 3.2 and Table 4] The feature names are not fully consistent between Table 1 and the regression rows in Table 5 (for example, "Places you visit" versus "Places you Visit"); harmonize the naming so readers can map each regression coefficient back to the corresponding vignette visualization.
  4. [Section 10] The text calls the sample "U.S. representative," while Table 4 shows notable deviations for Hispanic participants (7.6% versus 14.9% of the census) and for the education-by-race cells; "approximately representative" with the weighting description would be a more precise characterization.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the paper reports observational survey measurements, and its predictive model is an honest held-out evaluation on the same data, not a derivation from first principles.

full rationale

The paper's chain is measurement, not derivation. The headline findings are estimated associations from a mixed-effects ordinal regression of self-reported comfort on vignette features, actors, purposes, demographics, and attitudes; the dependent variable is the survey response itself, and no independent variable is constructed from that same outcome. The 'predictive task' in Section 9 fits classifiers on an 80/20 split of the collected survey responses and reports held-out F1 = 0.6. Because the split and the features are disclosed, this is standard out-of-sample evaluation within the dataset, not a fitted parameter renamed as an external prediction. The abstract's phrasing 'predictive task' is loose, but the method section makes the within-sample nature clear, so it does not constitute fitted-input-called-prediction. The cited prior work, including Martin and Nissenbaum, supplies the contextual integrity framing and the actor/purpose vocabularies, but the paper's central comparisons (detailed vs. obfuscated features, trajectory vs. POI features, demographic differences) are measured from the authors' own survey responses and do not derive their content from those citations. Self-citations in the literature review are used as examples of mobility-data analytics and are not load-bearing for the paper's conclusions. The post hoc exclusion of free-text responses that 'did not clearly match' the selected Likert rating is a potential validity threat to the regression estimates, but it is not a circularity: the filter is applied to the justification of the outcome, not used to define an input feature from the outcome. Similarly, the vignette-comprehension assumption is an operationalization assumption, not a circular reduction. No equation, coefficient, or stated result reduces to its own input by construction.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The paper introduces no new entities or physical constructs. The main researcher-chosen parameters are the response-time cutoff and manual exclusion criteria, both of which affect the analyzed sample. The regression model uses standard statistical assumptions and post-stratification weights based on census data.

free parameters (3)
  • Response time exclusion threshold = 10 seconds
    Chosen by hand to filter inattentive responses; affects sample size and potentially comfort distributions (Section 5).
  • Manual quality-exclusion judgment = Not specified
    Two researchers removed answers whose free-form text did not match the Likert rating; no inter-rater reliability or count of exclusions reported (Section 5).
  • Post-stratification weights = e.g., 1.72 for White under-high-school stratum
    Weights computed from U.S. Census proportions to adjust for underrepresented Hispanic and Asian groups; used in the ordinal regression (Appendix A).
assumptions (4)
  • standard math Ordinal Likert responses can be modeled by mixed-effects ordinal logistic regression with proportional odds assumptions.
    Assumption tested via nominal_test and scale_test; VIF < 2 (Appendix A).
  • domain assumption Factorial vignette responses reflect general privacy preferences beyond the specific vignette (contextual integrity).
    The study follows Nissenbaum's contextual integrity framework; prior work used similar vignette designs (Sections 1, 3).
  • domain assumption The obfuscation descriptions accurately represent current data broker and aggregator practices.
    The paper cites Cuebiq, Spectus, and SafeGraph as sources for obfuscation practices, but does not independently verify that the vignette visualizations match those practices (Table 1, Section 3).
  • domain assumption Post-stratification weighting corrects for demographic imbalance without residual bias.
    Hispanic respondents are underrepresented (7.6% survey vs 14.9% census); weights are applied, but non-response bias within strata may remain (Table 4, Appendix A).

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Cite this review

Pith. "Pith review of From "I have nothing to hide" to "It looks like stalking": Measuring Americans' Level of Comfort with Individual Mobility Features Extracted from Location Data." pith.science (2026). https://pith.science/paper/BZWZPEY6

@misc{pith2026250205686,
  author       = {Pith},
  title        = {Pith review of: From "I have nothing to hide" to "It looks like stalking": Measuring Americans' Level of Comfort with Individual Mobility Features Extracted from Location Data},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/BZWZPEY6}},
  note         = {Machine review of arXiv:2502.05686}
}
read the original abstract

Location data collection has become widespread with smart phones becoming ubiquitous. Smart phone apps often collect precise location data from users by offering \textit{free} services and then monetize it for advertising and marketing purposes. While major tech companies only sell aggregate behaviors for marketing purposes; data aggregators and data brokers offer access to individual location data. Some data brokers and aggregators have certain rules in place to preserve privacy; and the FTC has also started to vigorously regulate consumer privacy for location data. In this paper, we present an in-depth exploration of U.S. privacy perceptions with respect to specific location features derivable from data made available by location data brokers and aggregators. These results can provide policy implications that could assist organizations like the FTC in defining clear access rules. Using a factorial vignette survey, we collected responses from 1,405 participants to evaluate their level of comfort with sharing different types of location features, including individual trajectory data and visits to points of interest, available for purchase from data brokers worldwide. Our results show that trajectory-related features are associated with higher privacy concerns, that some data broker based obfuscation practices increase levels of comfort, and that race, ethnicity and education have an effect on data sharing privacy perceptions. We also model the privacy perceptions of people as a predictive task with F1 score \textbf{0.6}.

Figures

Figures reproduced from arXiv: 2502.05686 by the authors.

Figure 1
Figure 1. Percentage of responses per feature type and level [PITH_FULL_IMAGE:figures/full_fig_p008_1.png] view at source ↗
Figure 2
Figure 2. Percentage of responses per actor and level of com [PITH_FULL_IMAGE:figures/full_fig_p017_2.png] view at source ↗
Figure 3
Figure 3. Percentage of responses per purpose and level (or [PITH_FULL_IMAGE:figures/full_fig_p020_3.png] view at source ↗
Figures from the paper (15 more)
Figure 4
Figure 4. Figure 4: Popup showing the consent page and Internal Review Board (IRB) document link [PITH_FULL_IMAGE:figures/full_fig_p021_4.png]
Figure 5
Figure 5. Figure 5: The Introduction page 21 [PITH_FULL_IMAGE:figures/full_fig_p021_5.png]
Figure 6
Figure 6. Figure 6: Example of Vignette Question (Actor: Law enforcement agency- like city police department or a county sheriff’s office, [PITH_FULL_IMAGE:figures/full_fig_p024_6.png]
Figure 7
Figure 7. Figure 7: Example of Vignette Question (Actor:Commercial entity, Purpose: Personal wellness and physical activity, Feature: [PITH_FULL_IMAGE:figures/full_fig_p025_7.png]
Figure 8
Figure 8. Figure 8: Privacy attitude questions as seen by the survey participants [PITH_FULL_IMAGE:figures/full_fig_p026_8.png]
Figure 9
Figure 9. Figure 9: Demographic and technical knowledge questions as seen by the survey participants [PITH_FULL_IMAGE:figures/full_fig_p027_9.png]
Figure 10
Figure 10. Figure 10: Snapshot of interactive map with home Feature [PITH_FULL_IMAGE:figures/full_fig_p030_10.png]
Figure 14
Figure 14. Figure 14: Snapshot of interactive map with most Places of [PITH_FULL_IMAGE:figures/full_fig_p030_14.png]
Figure 15
Figure 15. Figure 15: Chart with Places of Visit (Ob) feature (Visits(Ob)) [PITH_FULL_IMAGE:figures/full_fig_p030_15.png]
Figure 18
Figure 18. Figure 18: Chart with modes of transportation Feature (Trans [PITH_FULL_IMAGE:figures/full_fig_p031_18.png]
Figure 17
Figure 17. Figure 17: Snapshot of interactive map with transportation [PITH_FULL_IMAGE:figures/full_fig_p031_17.png]
Figure 22
Figure 22. Figure 22: Snapshot of interactive map with Most frequent [PITH_FULL_IMAGE:figures/full_fig_p032_22.png]
Figure 21
Figure 21. Figure 21: Snapshot of interactive map with Most frequent [PITH_FULL_IMAGE:figures/full_fig_p032_21.png]
Figure 26
Figure 26. Figure 26: Snapshot of interactive map with International [PITH_FULL_IMAGE:figures/full_fig_p033_26.png]
Figure 27
Figure 27. Figure 27: Snapshot of interactive map with International [PITH_FULL_IMAGE:figures/full_fig_p033_27.png]

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Pith tools

Reviewed August 8, 2026 · model on record in the stance chip above.