Privacy Limitations Of Interest-based Advertising On The Web: A
Post-mortem Empirical Analysis Of Google's FLoC
- URL: http://arxiv.org/abs/2201.13402v6
- Date: Thu, 13 Oct 2022 20:44:38 GMT
- Title: Privacy Limitations Of Interest-based Advertising On The Web: A
Post-mortem Empirical Analysis Of Google's FLoC
- Authors: Alex Berke and Dan Calacci
- Abstract summary: In 2020, Google announced it would disable third-party cookies in the Chrome browser to improve user privacy.
In 2022, after testing FLoC in a real world trial, Google canceled the proposal with little explanation.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In 2020, Google announced it would disable third-party cookies in the Chrome
browser to improve user privacy. In order to continue to enable interest-based
advertising while mitigating risks of individualized user tracking, Google
proposed FLoC. The FLoC algorithm assigns users to "cohorts" that represent
groups of users with similar browsing behaviors so that ads can be served to
users based on their cohort. In 2022, after testing FLoC in a real world trial,
Google canceled the proposal with little explanation. In this work, we provide
a post-mortem analysis of two critical privacy risks for FloC by applying an
implementation of FLoC to a browsing dataset collected from over 90,000 U.S.
devices over a one year period. First, we show how, contrary to its privacy
goals, FLoC would have enabled cross-site user tracking by providing a unique
identifier for users available across sites, similar to the third-party cookies
FLoC was meant to be an improvement over. We show how FLoC cohort ID sequences
observed over time can provide this identifier to trackers, even with
third-party cookies disabled. We estimate the number of users in our dataset
that could be uniquely identified by FLoC IDs is more than 50% after 3 weeks
and more than 95% after 4 weeks. We also show how these risks increase when
cohort data are combined with browser fingerprinting, and how our results
underestimate the true risks FLoC would have posed in a real-world deployment.
Second, we examine the risk of FLoC leaking sensitive demographic information.
Although we find statistically significant differences in browsing behaviors
between demographic groups, we do not find that FLoC significantly risks
exposing race or income information about users in our dataset. Our
contributions provide insights and example analyses for future approaches that
seek to protect user privacy while monetizing the web.
Related papers
- PriRoAgg: Achieving Robust Model Aggregation with Minimum Privacy Leakage for Federated Learning [49.916365792036636]
Federated learning (FL) has recently gained significant momentum due to its potential to leverage large-scale distributed user data.
The transmitted model updates can potentially leak sensitive user information, and the lack of central control of the local training process leaves the global model susceptible to malicious manipulations on model updates.
We develop a general framework PriRoAgg, utilizing Lagrange coded computing and distributed zero-knowledge proof, to execute a wide range of robust aggregation algorithms while satisfying aggregated privacy.
arXiv Detail & Related papers (2024-07-12T03:18:08Z) - A Quantitative Information Flow Analysis of the Topics API [0.34952465649465553]
We analyze the re-identification risk for individual Internet users introduced by the Topics API from the perspective of information- and decision-theoretic framework.
Our model allows a theoretical analysis of both privacy and utility aspects of the API and their trade-off, and we show that the Topics API does have better privacy than third-party cookies.
arXiv Detail & Related papers (2023-09-26T08:14:37Z) - Mitigating Cross-client GANs-based Attack in Federated Learning [78.06700142712353]
Multi distributed multimedia clients can resort to federated learning (FL) to jointly learn a global shared model.
FL suffers from the cross-client generative adversarial networks (GANs)-based (C-GANs) attack.
We propose Fed-EDKD technique to improve the current popular FL schemes to resist C-GANs attack.
arXiv Detail & Related papers (2023-07-25T08:15:55Z) - FheFL: Fully Homomorphic Encryption Friendly Privacy-Preserving
Federated Learning with Byzantine Users [13.924829298309415]
federated learning (FL) technique was developed to mitigate data privacy issues in the traditional machine learning paradigm.
Next-generation FL architectures proposed encryption and anonymization techniques to protect the model updates from the server.
This paper proposes a novel FL algorithm based on a fully homomorphic encryption (FHE) scheme.
arXiv Detail & Related papers (2023-06-08T11:20:00Z) - On the Robustness of Topics API to a Re-Identification Attack [6.157783777246449]
Google proposed the Topics API framework as a privacy-friendly alternative for behavioural advertising.
This paper evaluates the robustness of the Topics API to a re-identification attack.
We find that the Topics API mitigates but cannot prevent re-identification to take place, as there is a sizeable chance that a user's profile is unique within a website's audience.
arXiv Detail & Related papers (2023-06-08T10:53:48Z) - 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) - Privacy-Preserving Online Content Moderation: A Federated Learning Use
Case [3.1925030748447747]
Federated Learning (FL) is an ML paradigm where the training is performed locally on the users' devices.
We propose a privacy-preserving FL framework for online content moderation that incorporates Differential Privacy (DP)
We show that the proposed FL framework can be close to the centralized approach - for both the DP and non-DP FL versions.
arXiv Detail & Related papers (2022-09-23T20:12:18Z) - Unraveling the Connections between Privacy and Certified Robustness in
Federated Learning Against Poisoning Attacks [68.20436971825941]
Federated learning (FL) provides an efficient paradigm to jointly train a global model leveraging data from distributed users.
Several studies have shown that FL is vulnerable to poisoning attacks.
To protect the privacy of local users, FL is usually trained in a differentially private way.
arXiv Detail & Related papers (2022-09-08T21:01:42Z) - Decepticons: Corrupted Transformers Breach Privacy in Federated Learning
for Language Models [58.631918656336005]
We propose a novel attack that reveals private user text by deploying malicious parameter vectors.
Unlike previous attacks on FL, the attack exploits characteristics of both the Transformer architecture and the token embedding.
arXiv Detail & Related papers (2022-01-29T22:38:21Z) - Masked LARk: Masked Learning, Aggregation and Reporting worKflow [6.484847460164177]
Many web advertising data flows involve passive cross-site tracking of users.
Most browsers are moving towards removal of 3PC in subsequent browser iterations.
We propose a new proposal, called Masked LARk, for aggregation of user engagement measurement and model training.
arXiv Detail & Related papers (2021-10-27T21:59:37Z) - 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.