Privacy Preference Signals: Past, Present and Future
- URL: http://arxiv.org/abs/2106.02283v4
- Date: Wed, 14 Jul 2021 10:48:17 GMT
- Title: Privacy Preference Signals: Past, Present and Future
- Authors: Maximilian Hils, Daniel W. Woods, Rainer B\"ohme (University of
Innsbruck)
- Abstract summary: This paper integrates post- developments into wider history of privacy preference signals.
Our main contribution is a high-frequency longitudinal study describing how Transparency and Consent Framework (TCF) signal gained dominance.
Both the number of third parties on a website and the presence of Google Ads are associated with higher adoption of TCF.
- Score: 2.2559617939136505
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Privacy preference signals are digital representations of how users want
their personal data to be processed. Such signals must be adopted by both the
sender (users) and intended recipients (data processors). Adoption represents a
coordination problem that remains unsolved despite efforts dating back to the
1990s. Browsers implemented standards like the Platform for Privacy Preferences
(P3P) and Do Not Track (DNT), but vendors profiting from personal data faced
few incentives to receive and respect the expressed wishes of data subjects. In
the wake of recent privacy laws, a coalition of AdTech firms published the
Transparency and Consent Framework (TCF), which defines an opt-in consent
signal. This paper integrates post-GDPR developments into the wider history of
privacy preference signals. Our main contribution is a high-frequency
longitudinal study describing how TCF signal gained dominance as of February
2021. We explore which factors correlate with adoption at the website level.
Both the number of third parties on a website and the presence of Google Ads
are associated with higher adoption of TCF. Further, we show that vendors acted
as early adopters of TCF 2.0 and provide two case-studies describing how
Consent Management Providers shifted existing customers to TCF 2.0. We sketch
ways forward for a pro-privacy signal.
Related papers
- Collaborative Inference over Wireless Channels with Feature Differential Privacy [57.68286389879283]
Collaborative inference among multiple wireless edge devices has the potential to significantly enhance Artificial Intelligence (AI) applications.
transmitting extracted features poses a significant privacy risk, as sensitive personal data can be exposed during the process.
We propose a novel privacy-preserving collaborative inference mechanism, wherein each edge device in the network secures the privacy of extracted features before transmitting them to a central server for inference.
arXiv Detail & Related papers (2024-10-25T18:11:02Z) - PrivacyLens: Evaluating Privacy Norm Awareness of Language Models in Action [54.11479432110771]
PrivacyLens is a novel framework designed to extend privacy-sensitive seeds into expressive vignettes and further into agent trajectories.
We instantiate PrivacyLens with a collection of privacy norms grounded in privacy literature and crowdsourced seeds.
State-of-the-art LMs, like GPT-4 and Llama-3-70B, leak sensitive information in 25.68% and 38.69% of cases, even when prompted with privacy-enhancing instructions.
arXiv Detail & Related papers (2024-08-29T17:58:38Z) - Differentially Private Data Release on Graphs: Inefficiencies and Unfairness [48.96399034594329]
This paper characterizes the impact of Differential Privacy on bias and unfairness in the context of releasing information about networks.
We consider a network release problem where the network structure is known to all, but the weights on edges must be released privately.
Our work provides theoretical foundations and empirical evidence into the bias and unfairness arising due to privacy in these networked decision problems.
arXiv Detail & Related papers (2024-08-08T08:37:37Z) - DiffAudit: Auditing Privacy Practices of Online Services for Children and Adolescents [5.609870736739224]
Children's and adolescents' online data privacy are regulated by laws such as the Children's Online Privacy Protection Act (COPPA)
Online services directed towards children, adolescents, and adults must comply with these laws.
We present DiffAudit, a platform-agnostic privacy auditing methodology for general audience services.
arXiv Detail & Related papers (2024-06-10T17:14:53Z) - Privacy Policies and Consent Management Platforms: Growth and Users'
Interactions over Time [4.356242302111725]
Consent platforms (CMPs) have emerged as practical solutions to make it easier for website administrators to manage user consent.
This paper presents a detailed analysis of the evolution of CMPs spanning nine years.
We observe how even small changes in the design of Privacy Banners have a critical impact on the user's giving or denying their consent to data collection.
arXiv Detail & Related papers (2024-02-28T13:36:27Z) - Group Decision-Making among Privacy-Aware Agents [2.4401219403555814]
Preserving individual privacy and enabling efficient social learning are both important desiderata but seem fundamentally at odds with each other.
We do so by controlling information leakage using rigorous statistical guarantees that are based on differential privacy (DP)
Our results flesh out the nature of the trade-offs in both cases between the quality of the group decision outcomes, learning accuracy, communication cost, and the level of privacy protections that the agents are afforded.
arXiv Detail & Related papers (2024-02-13T01:38:01Z) - The Fair Value of Data Under Heterogeneous Privacy Constraints in
Federated Learning [26.53734856637336]
This paper puts forth an idea for a textitfair amount to compensate users for their data at a given privacy level based on an axiomatic definition of fairness.
We also formulate a heterogeneous federated learning problem for the platform with privacy level options for users.
arXiv Detail & Related papers (2023-01-30T23:51:03Z) - Privacy-Preserving Graph Convolutional Networks for Text Classification [3.5503507997334958]
Graphal convolution networks (GCNs) are a powerful architecture for representation learning and making predictions on documents that naturally occur as graphs.
Data containing sensitive personal information, such as documents with people's profiles or relationships as edges, are prone to privacy leaks from GCNs.
We show that privacy-preserving GCNs perform up to 90% of their non-private variants, while formally guaranteeing strong privacy measures.
arXiv Detail & Related papers (2021-02-10T15:27:38Z) - Second layer data governance for permissioned blockchains: the privacy
management challenge [58.720142291102135]
In pandemic situations, such as the COVID-19 and Ebola outbreak, the action related to sharing health data is crucial to avoid the massive infection and decrease the number of deaths.
In this sense, permissioned blockchain technology emerges to empower users to get their rights providing data ownership, transparency, and security through an immutable, unified, and distributed database ruled by smart contracts.
arXiv Detail & Related papers (2020-10-22T13:19:38Z) - BeeTrace: A Unified Platform for Secure Contact Tracing that Breaks Data
Silos [73.84437456144994]
Contact tracing is an important method to control the spread of an infectious disease such as COVID-19.
Current solutions do not utilize the huge volume of data stored in business databases and individual digital devices.
We propose BeeTrace, a unified platform that breaks data silos and deploys state-of-the-art cryptographic protocols to guarantee privacy goals.
arXiv Detail & Related papers (2020-07-05T10:33:45Z) - Beyond privacy regulations: an ethical approach to data usage in
transportation [64.86110095869176]
We describe how Federated Machine Learning can be applied to the transportation sector.
We see Federated Learning as a method that enables us to process privacy-sensitive data, while respecting customer's privacy.
arXiv Detail & Related papers (2020-04-01T15:10:12Z)
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.