The DOI matches its Crossref record.
Obfuscation: A User's Guide for Privacy and Protest
Abstract
How we can evade, protest, and sabotage today's pervasive digital surveillance by deploying more data, not less—and why we should. With Obfuscation, Finn Brunton and Helen Nissenbaum mean to start a revolution. They are calling us not to the barricades but to our computers, offering us ways to fight today's pervasive digital surveillance—the collection of our data by governments, corporations, advertisers, and hackers. To the toolkit of privacy protecting techniques and projects, they propose adding obfuscation: the deliberate use of ambiguous, confusing, or misleading information to interfere with surveillance and data collection projects. Brunton and Nissenbaum provide tools and a rationale for evasion, noncompliance, refusal, even sabotage—especially for average users, those of us not in a position to opt out or exert control over data about ourselves. Obfuscation will teach users to push back, software developers to keep their user data safe, and policy makers to gather data without misusing it. Brunton and Nissenbaum present a guide to the forms and formats that obfuscation has taken and explain how to craft its implementation to suit the goal and the adversary. They describe a series of historical and contemporary examples, including radar chaff deployed by World War II pilots, Twitter bots that hobbled the social media strategy of popular protest movements, and software that can camouflage users' search queries and stymie online advertising. They go on to consider obfuscation in more general terms, discussing why obfuscation is necessary, whether it is justified, how it works, and how it can be integrated with other privacy practices and technologies.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Veale, M., & Binns, R. (2017) · Big Data & Society · doi:10.1177/2053951717743530 · Cited by 512
Veale, M., & Binns, R. (2017) · doi:10.31235/osf.io/ustxg · Cited by 512
Newlands, G. (2021) · Organization Studies · doi:10.1177/0170840620937900 · Cited by 439
Where are human subjects in big data research? the emerging ethics divide
Metcalf, J., & Crawford, K. (2016) · Big Data & Society · doi:10.1177/2053951716650211 · Cited by 394
Algorithmically recognizable: Santorum’s Google problem, and Google’s Santorum problem
Gillespie, T. (2017) · Information, Communication & Society · doi:10.1080/1369118x.2016.1199721 · Cited by 178
Metadata
- Authors
- Brunton, F., & Nissenbaum, H.
- Venue
- The MIT Press eBooks
- Publisher
- The MIT Press
- Published
- 2015-09-04
- OpenAlex
- W4248167275
- Cited by
- 256
- References
- 0
- FWCI
- 20.62
- Type
- book
- Language
- en
- Open access
- closed
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Obfuscation: A User's Guide for Privacy and Protest. https://app.cochat.ai/papers/10.7551/mitpress/9780262029735.001.0001
- The paper
- Brunton, F., & Nissenbaum, H. (2015). Obfuscation: A User's Guide for Privacy and Protest. The MIT Press eBooks. https://doi.org/10.7551/mitpress/9780262029735.001.0001
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.