How Much Is Too Much? Leveraging Ads Audience Estimation to Evaluate Public Profile Uniqueness
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The experiments rely on a baseline of 1,408 random accounts from Chen et al. [14] used to form a baseline of 'normal' accounts.
“In our experiments, we will also rely on a baseline of 1,408 random accounts from Chen et al. [14] which we use to form a baseline of 'normal' accounts.”
From Measuring, Characterizing, and Detecting Facebook Like Farms (2017) · cites this paper as [14]
Cites
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Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
De Cristofaro, E., Friedman, A., Jourjon, G., Kaafar, M. A., & Shafiq, M. Z. (2014) · Proceedings of the 2014 Conference on Internet Measurement Conference · doi:10.1145/2663716.2663729 · Cited by 85 · see citing works
“The original sample was crawled for another project [9], obtained by randomly sampling Facebook public directory which lists all the IDs of searchable profiles.”
Self-citationMeasuring, Characterizing, and Detecting Facebook Like Farms
Ikram, M., Onwuzurike, L., Farooqi, S., Cristofaro, E. D., Friedman, A., Jourjon, G., Kaafar, M. A., & Shafiq, M. Z. (2017) · ACM Transactions on Privacy and Security · doi:10.1145/3121134 · Cited by 27 · see citing works
“[14] which we use to form a baseline of “normal” accounts.”
MethodSelf-citation
- Authors
- Chen, T., Chaabane, A., Tournoux, P. U., Kaafar, M.-A., & Boreli, R.
- Affiliations
- UNSW Sydney (AU)
- Institut national de recherche en sciences et technologies du numérique (FR)
- University of Reunion Island (RE)
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2013
- Volume
- 7981
- Pages
- 225-244
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W131591713
- Cited by
- 21
- References
- 25
- Citations by year
- 2024: 1 · 2023: 1 · 2021: 5 · 2020: 4 · 2018: 5 · 2017: 2 · 2015: 1 · 2014: 2
- FWCI
- 3.06
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- HAL (submittedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Social Sciences › Decision Sciences › Management Science and Operations Research › Data Quality and Management
- Keywords
- social networking sites, user identification, Facebook, data anonymization, data disclosure, privacy
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