poster

Poster: Analysis of User Uniqueness on LinkedIn Based on Publicly Available Non-PII

Authors:

Ángel Merino,

José González-Cabañas,

Ángel Cuevas,

Rubén CuevasAuthors Info & Claims

IMC '23: Proceedings of the 2023 ACM on Internet Measurement Conference

Pages 726 - 727

https://doi.org/10.1145/3618257.3625000

Published: 24 October 2023 Publication History

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Abstract

The literature has shown combining a few non-Personal Identifiable Information (non-PII) is enough to make a user unique in a dataset including millions of users. In this work, we demonstrate that the combination of the location and 6 rare (14 random) skills in a LinkedIn profile is enough to become unique in a user base of ~800M users with a probability of 75%. The novelty is these attributes are publicly accessible to anyone registered on LinkedIn and could be activated through advertising campaigns.

References

[1]

de Montjoye, Y.-A., Hidalgo, C. A., Verleysen, M., and Blondel, V. D. Unique in the Crowd: The privacy bounds of human mobility. Scientific reports 3, 1 (2013), 1376.

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[2]

De Montjoye, Y.-A., Radaelli, L., Singh, V. K., et al. Unique in the shopping mall: On the reidentifiability of credit card metadata. Science 347, 6221 (2015), 536--539.

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[3]

EU. Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation), Apr. 2016.

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[4]

González-Cabañas, J., Cuevas, A., Cuevas, R., López-Fernández, J., and García, D. Unique on facebook: Formulation and evidence of (nano)targeting individual users with non-pii data. In Proceedings of the 21st ACM Internet Measurement Conference (New York, NY, USA, 2021), IMC '21, Association for Computing Machinery, p. 464--479.

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[5]

Rocher, L., Hendrickx, J. M., and de Montjoye, Y.-A. Estimating the success of re-identifications in incomplete datasets using generative models. Nature Communications 10, 1 (2019), 3069.

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Index Terms

Poster: Analysis of User Uniqueness on LinkedIn Based on Publicly Available Non-PII
1. Security and privacy
  1. Human and societal aspects of security and privacy
    1. Privacy protections
2. Social and professional topics
  1. Computing / technology policy
    1. Privacy policies

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Published In

IMC '23: Proceedings of the 2023 ACM on Internet Measurement Conference

October 2023

746 pages

ISBN:9798400703829

DOI:10.1145/3618257

General Chairs:
Marie-José Montpetit
McGill University, Canada
,
Aris Leivadeas
École de Technologie Supérieure, Canada
,
Program Chairs:
Steve Uhlig
Queen Mary University of London, United Kingdom
,
Mobin Javed
Lahore University of Management Sciences, Pakistan

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 October 2023

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IMC '23

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SIGCOMM

IMC '23: ACM Internet Measurement Conference

October 24 - 26, 2023

Montreal QC, Canada

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Overall Acceptance Rate 277 of 1,083 submissions, 26%

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Analysis and Implementation of Nanotargeting on LinkedIn Based on Publicly Available Non-PII

User Disposition and Attitude towards Advertisements Placed in Facebook, LinkedIn, Twitter and YouTube: A Decision Tree and MANOVA Approach

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