A Contextual Approach to Privacy Online
Abstract
Recent media revelations have demonstrated the extent of third-party tracking and monitoring online, much of it spurred by data aggregation, profiling, and selective targeting. How to protect privacy online is a frequent question in public discourse and has reignited the interest of government actors. In the United States, notice-and-consent remains the fallback approach in online privacy policies, despite its weaknesses. This essay presents an alternative approach, rooted in the theory of contextual integrity. Proposals to improve and fortify notice-and-consent, such as clearer privacy policies and fairer information practices, will not overcome a fundamental flaw in the model, namely, its assumption that individuals can understand all facts relevant to true choice at the moment of pair-wise contracting between individuals and data gatherers. Instead, we must articulate a backdrop of context-specific substantive norms that constrain what information websites can collect, with whom they can share it, and under what conditions it can be shared. In developing this approach, the paper warns that the current bias in conceiving of the Net as a predominantly commercial enterprise seriously limits the privacy agenda.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Providing users with more explicit choices and consent can enhance their agency and control.
“Alternatively, providing users with more explicit choices and consent can enhance their agency and control [23,25].”
From Understanding Users' Privacy Perceptions Towards LLM's RAG-based Memory (2025) · cites this paper as [25]
Consent in the online space is problematic; users often lack the choice and are presented with a 'take it or leave it' option when accessing online services.
“Finally, consent in the online space is also problematic; users often lack the choice and are presented with a ‘take it or leave it’ option when accessing online services (Nissenbaum 2011; Taddeo and Floridi 2015).”
From How to Design AI for Social Good: Seven Essential Factors (2020) · cites this paper as Nissenbaum 2011
Cites
(2010) · Econometrica · doi:10.3982/ecta782forth · Cited by 1 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Big other: Surveillance Capitalism and the Prospects of an Information Civilization
Zuboff, S. (2015) · Journal of Information Technology · doi:10.1057/jit.2015.5 · Cited by 2,978 · see citing works
‘Hypernudge’: big Data as a mode of regulation by design
Yeung, K. (2017) · Information, Communication & Society · doi:10.1080/1369118x.2016.1186713 · Cited by 868 · see citing works
Social media definition and the governance challenge: An introduction to the special issue
Obar, J. A., & Wildman, S. (2015) · Telecommunications Policy · doi:10.1016/j.telpol.2015.07.014 · Cited by 650 · see citing works
How to Design AI for Social Good: Seven Essential Factors
Floridi, L., Cowls, J., King, T. C., & Taddeo, M. (2020) · Science and Engineering Ethics · doi:10.1007/s11948-020-00213-5 · Cited by 430 · see citing works
“Finally, consent in the online space is also problematic; users often lack the choice and are presented with a ‘take it or leave it’ option when accessing online services (Nissenbaum 2011; Taddeo and Floridi 2015).”
BackgroundBig Data’s End Run around Anonymity and Consent
Barocas, S., & Nissenbaum, H. (2014) · Privacy, Big Data, and the Public Good · doi:10.1017/cbo9781107590205.004 · Cited by 320 · see citing works
Self-citationvan Ooijen, I., & Vrabec, H. U. (2019) · Journal of Consumer Policy · doi:10.1007/s10603-018-9399-7 · Cited by 180 · see citing works
Buhmann, A., Paßmann, J., & Fieseler, C. (2020) · Journal of Business Ethics · doi:10.1007/s10551-019-04226-4 · Cited by 167 · see citing works
Student Vulnerability, Agency and Learning Analytics: An Exploration
Prinsloo, P., & Slade, S. (2016) · Journal of Learning Analytics · doi:10.18608/jla.2016.31.10 · Cited by 147 · see citing works
Formosa, P. (2021) · Minds and Machines · doi:10.1007/s11023-021-09579-2 · Cited by 144 · see citing works
Fit for purpose? The GDPR and the governance of European digital health
Marelli, L., Lievevrouw, E., & Van Hoyweghen, I. (2020) · Policy Studies · doi:10.1080/01442872.2020.1724929 · Cited by 137 · see citing works
Big data’s end run around procedural privacy protections
Barocas, S., & Nissenbaum, H. (2014) · Communications of the ACM · doi:10.1145/2668897 · Cited by 129 · see citing works
Self-citationA principlist framework for cybersecurity ethics
Formosa, P., Wilson, M., & Richards, D. (2021) · Computers & Security · doi:10.1016/j.cose.2021.102382 · Cited by 105 · see citing works
Contestable AI by Design: Towards a Framework
Alfrink, K., Keller, I., Kortuem, G., & Doorn, N. (2022) · Minds and Machines · doi:10.1007/s11023-022-09611-z · Cited by 104 · see citing works
“It also seeks to strike a balance between usability and comprehensiveness, in an effort to avoid the “transparency paradox” (Nissenbaum, 2011 in Crawford, 2016).”
Students' privacy concerns in learning analytics: Model development
Mutimukwe, C., Viberg, O., Oberg, L., & Cerratto‐Pargman, T. (2022) · British Journal of Educational Technology · doi:10.1111/bjet.13234 · Cited by 90 · see citing works
Cerratto Pargman, T., & McGrath, C. (2021) · Journal of Learning Analytics · doi:10.18608/jla.2021.1 · Cited by 88 · see citing works
Mühlhoff, R. (2023) · Big Data & Society · doi:10.1177/20539517231166886 · Cited by 85 · see citing works
Hargittai, E., & Litt, E. (2013) · IEEE Security & Privacy · doi:10.1109/msp.2013.64 · Cited by 79 · see citing works
Caught in the web: informed consent for online health research
Vayena, E., Mastroianni, A., & Kahn, J. (2013) · Science Translational Medicine · doi:10.1126/scitranslmed.3004798 · Cited by 53 · see citing works
Cybervetting job applicants on social media: the new normal?
Jacobson, J., & Gruzd, A. (2020) · Ethics and Information Technology · doi:10.1007/s10676-020-09526-2 · Cited by 44 · see citing works
Hofman, D., Lemieux, V. L., Joo, A., & Batista, D. A. (2019) · Records Management Journal · doi:10.1108/rmj-12-2018-0045 · Cited by 40 · see citing works
Asthana, S., Im, J., Chen, Z., & Banovic, N. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642180 · Cited by 34 · see citing works
Understanding Users' Privacy Perceptions Towards LLM's RAG-based Memory
Zhang, S., Ma, R., Ma, Y., Li, S., Xu, Y., Yi, X., & Li, H. (2025) · Proceedings of the 2025 Workshop on Human-Centered AI Privacy and Security · doi:10.1145/3733816.3760750 · Cited by 6 · see citing works
“Alternatively, providing users with more explicit choices and consent can enhance their agency and control [23, 25].”
- Authors
- Nissenbaum, H.
- Affiliations
- New York University (US)
- Corresponding author
- Nissenbaum, Helen
- ORCID
- Nissenbaum, Helen
- Venue
- Daedalus
- Publisher
- MIT Press - Journals
- Published
- 2011-10
- Volume
- 140
- Issue
- 4
- Pages
- 32-48
- ISSN
- 0011-5266 (ISSN-L)
- 1548-6192
- OpenAlex
- W3125057561
- Cited by
- 649
- References
- 1
- Citations by year
- 2026: 47 · 2025: 58 · 2024: 54 · 2023: 49 · 2022: 57 · 2021: 55 · 2020: 56 · 2019: 67 · 2018: 42 · 2017: 28 · 2016: 42 · 2015: 41 · 2014: 27 · 2013: 19 · 2012: 7
- FWCI
- 56.58
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Daedalus (publishedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Social Sciences › Law › Digitalization, Law, and Regulation
- Physical Sciences › Computer Science › Information Systems › COVID-19 Digital Contact Tracing
- Keywords
- online privacy, contextual integrity, notice and consent, third-party tracking, data aggregation, profiling, selective targeting, privacy policies, information sharing, commercial internet
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