Unique on Facebook: Formulation and Evidence of (Nano)targeting
Individual Users with non-PII Data
- URL: http://arxiv.org/abs/2110.06636v2
- Date: Sat, 16 Oct 2021 16:27:51 GMT
- Title: Unique on Facebook: Formulation and Evidence of (Nano)targeting
Individual Users with non-PII Data
- Authors: Jos\'e Gonz\'alez-Caba\~nas, \'Angel Cuevas, Rub\'en Cuevas, Juan
L\'opez-Fern\'andez, David Garc\'ia
- Abstract summary: We define a data-driven model to quantify the number of interests from a user that make them unique on Facebook.
To the best of our knowledge, this represents the first study of individuals' uniqueness at the world population scale.
We run an experiment through 21 Facebook ad campaigns that target three of the authors of this paper.
- Score: 0.10799106628248668
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: The privacy of an individual is bounded by the ability of a third party to
reveal their identity. Certain data items such as a passport ID or a mobile
phone number may be used to uniquely identify a person. These are referred to
as Personal Identifiable Information (PII) items. Previous literature has also
reported that, in datasets including millions of users, a combination of
several non-PII items (which alone are not enough to identify an individual)
can uniquely identify an individual within the dataset. In this paper, we
define a data-driven model to quantify the number of interests from a user that
make them unique on Facebook. To the best of our knowledge, this represents the
first study of individuals' uniqueness at the world population scale. Besides,
users' interests are actionable non-PII items that can be used to define ad
campaigns and deliver tailored ads to Facebook users. We run an experiment
through 21 Facebook ad campaigns that target three of the authors of this paper
to prove that, if an advertiser knows enough interests from a user, the
Facebook Advertising Platform can be systematically exploited to deliver ads
exclusively to a specific user. We refer to this practice as nanotargeting.
Finally, we discuss the harmful risks associated with nanotargeting such as
psychological persuasion, user manipulation, or blackmailing, and provide
easily implementable countermeasures to preclude attacks based on nanotargeting
campaigns on Facebook.
Related papers
- How Unique is Whose Web Browser? The role of demographics in browser fingerprinting among US users [50.699390248359265]
Browser fingerprinting can be used to identify and track users across the Web, even without cookies.
This technique and resulting privacy risks have been studied for over a decade.
We provide a first-of-its-kind dataset to enable further research.
arXiv Detail & Related papers (2024-10-09T14:51:58Z) - Evaluating Large Language Model based Personal Information Extraction and Countermeasures [63.91918057570824]
Large language model (LLM) can be misused by attackers to accurately extract various personal information from personal profiles.
LLM outperforms conventional methods at such extraction.
prompt injection can mitigate such risk to a large extent and outperforms conventional countermeasures.
arXiv Detail & Related papers (2024-08-14T04:49:30Z) - On mission Twitter Profiles: A Study of Selective Toxic Behavior [5.0157204307764625]
This study aims to characterize profiles potentially used for influence operations, termed 'on-mission profiles'
Longitudinal data from 138K Twitter or X, profiles and 293M tweets enables profiling based on theme diversity.
arXiv Detail & Related papers (2024-01-25T15:42:36Z) - FedDMF: Privacy-Preserving User Attribute Prediction using Deep Matrix
Factorization [1.9181612035055007]
We propose a novel algorithm for predicting user attributes without requiring user matching.
Our approach involves training deep matrix factorization models on different clients and sharing only attribute item vectors.
This allows us to predict user attributes without sharing the user vectors themselves.
arXiv Detail & Related papers (2023-12-24T06:49:00Z) - Protecting User Privacy in Online Settings via Supervised Learning [69.38374877559423]
We design an intelligent approach to online privacy protection that leverages supervised learning.
By detecting and blocking data collection that might infringe on a user's privacy, we can restore a degree of digital privacy to the user.
arXiv Detail & Related papers (2023-04-06T05:20:16Z) - How Do Input Attributes Impact the Privacy Loss in Differential Privacy? [55.492422758737575]
We study the connection between the per-subject norm in DP neural networks and individual privacy loss.
We introduce a novel metric termed the Privacy Loss-Input Susceptibility (PLIS) which allows one to apportion the subject's privacy loss to their input attributes.
arXiv Detail & Related papers (2022-11-18T11:39:03Z) - Subject Membership Inference Attacks in Federated Learning [4.377743737361996]
We propose two black-box attacks for subject membership inference.
We find our attacks to be extremely potent, even without access to exact training records.
We also investigate the effectiveness of Differential Privacy in mitigating this threat.
arXiv Detail & Related papers (2022-06-07T14:06:12Z) - Behind the Mask: Demographic bias in name detection for PII masking [5.071136834627255]
We evaluate the performance of three off-the-shelf PII masking systems on name detection and redaction.
We find that an open-source RoBERTa-based system shows fewer disparities than the commercial models we test.
The highest error rates occurred for names associated with Black and Asian/Pacific Islander individuals.
arXiv Detail & Related papers (2022-05-09T18:21:41Z) - Truth Serum: Poisoning Machine Learning Models to Reveal Their Secrets [53.866927712193416]
We show that an adversary who can poison a training dataset can cause models trained on this dataset to leak private details belonging to other parties.
Our attacks are effective across membership inference, attribute inference, and data extraction.
Our results cast doubts on the relevance of cryptographic privacy guarantees in multiparty protocols for machine learning.
arXiv Detail & Related papers (2022-03-31T18:06:28Z) - Challenges and approaches to privacy preserving post-click conversion
prediction [3.4071263815701336]
We provide an overview of the challenges and constraints when learning conversion models in this setting.
We introduce a novel approach for training these models that makes use of post-ranking signals.
We show using offline experiments on real world data that it outperforms a model relying on opt-in data alone.
arXiv Detail & Related papers (2022-01-29T21:36:01Z) - Keystroke Biometrics in Response to Fake News Propagation in a Global
Pandemic [77.79066811371978]
This work proposes and analyzes the use of keystroke biometrics for content de-anonymization.
Fake news have become a powerful tool to manipulate public opinion, especially during major events.
arXiv Detail & Related papers (2020-05-15T17:56:11Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.