The DOI matches its Crossref record; 26 of the 27 references it cites were found in a registry or an index.
Information revelation and privacy in online social networks
Abstract
Participation in social networking sites has dramatically increased in recent years. Services such as Friendster, Tribe, or the Facebook allow millions of individuals to create online profiles and share personal information with vast networks of friends - and, often, unknown numbers of strangers. In this paper we study patterns of information revelation in online social networks and their privacy implications. We analyze the online behavior of more than 4,000 Carnegie Mellon University students who have joined a popular social networking site catered to colleges. We evaluate the amount of information they disclose and study their usage of the site's privacy settings. We highlight potential attacks on various aspects of their privacy, and we show that only a minimal percentage of users changes the highly permeable privacy preferences.
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“Privacy breaches can have harmful outcomes (Barocas and Selbst, 2016) like stalking, discrimination, black-mailing or identity theft (Gross and Acquisti, 2005).”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Gross and Acquisti, 2005
In the citing paper
“Navigating the trade-offs between privacy, utility, and convenience lead users to willingly disclose large amounts of personal information while rarely adjusting privacy settings [17].”
From "I tell him everything that I do": An investigation of privacy and safety implications of AI companion usage (2025) · cites this paper as [17]
In the citing paper
“this issue can be exacerbated for Replika users given their emotional attachment to the app, hence, their increased readiness to tolerate privacy invasion in exchange to having access to their AI companion [17].”
From "I tell him everything that I do": An investigation of privacy and safety implications of AI companion usage (2025) · cites this paper as [17]
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Metadata
- Authors
- Gross, R., & Acquisti, A.
- Affiliations
- Carnegie Mellon University, Pittsburgh, PA
- Carnegie Mellon University (US)
- ORCID
- Acquisti, Alessandro
- Venue
- Proceedings of the 2005 ACM workshop on Privacy in the electronic society
- Publisher
- ACM
- Published
- 2005-11-07
- Pages
- 71-80
- OpenAlex
- W2016563917
- Cited by
- 2,028
- References
- 27
- Citations by year
- 2026: 12 · 2025: 22 · 2024: 32 · 2023: 42 · 2022: 48 · 2021: 61 · 2020: 70 · 2019: 79 · 2018: 130 · 2017: 127 · 2016: 125 · 2015: 167 · 2014: 176 · 2013: 200 · 2012: 202
- FWCI
- 57.31
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM Workshop on Privacy in the Electronic Society (WPES) (publishedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Information Systems › User Authentication and Security Systems
- Keywords
- social networking sites, privacy, information disclosure, privacy settings, privacy attacks, user behavior, university students
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 ›
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