Ownership and Flow Primitives for Scalable Consent Management in Digital Public Infrastructures
- URL: http://arxiv.org/abs/2511.02950v1
- Date: Tue, 04 Nov 2025 19:52:55 GMT
- Title: Ownership and Flow Primitives for Scalable Consent Management in Digital Public Infrastructures
- Authors: Rohith Vaidyanathan, Srinath Srinivasa, Praseeda, Dev Shinde,
- Abstract summary: Digital public infrastructures (DPIs) represent networks of open technology standards, applications, services, and digital assets made available for the public good.<n>One of the key challenges in DPI design is to resolve complex issues of consent, scaled over large populations.<n>This paper addresses the question of representing modes of ownership of digital assets and their corresponding implications for consensual data flows in a DPI.
- Score: 1.8899300124593648
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Digital public infrastructures (DPIs) represent networks of open technology standards, applications, services, and digital assets made available for the public good. One of the key challenges in DPI design is to resolve complex issues of consent, scaled over large populations. While the primary objective of consent management is to empower the data owner, ownership itself can come with variegated morphological forms with different implications over consent. Questions of ownership in a public space also have several nuances where individual autonomy needs to be balanced with public well-being and national sovereignty. This requires consent management to be compliant with applicable regulations for data sharing. This paper addresses the question of representing modes of ownership of digital assets and their corresponding implications for consensual data flows in a DPI. It proposes a set of foundational abstractions to represent them. Our proposed architecture responds to the growing need for transparent, secure, and user-centric consent management within Digital Public Infrastructure (DPI). Incorporating a formalised data ownership model enables end-to-end traceability of consent, fine-grained control over data sharing, and alignment with evolving legal and regulatory frameworks.
Related papers
- How to DP-fy Your Data: A Practical Guide to Generating Synthetic Data With Differential Privacy [52.00934156883483]
Differential Privacy (DP) is a framework for reasoning about and limiting information leakage.<n>Differentially Private Synthetic data refers to synthetic data that preserves the overall trends of source data.
arXiv Detail & Related papers (2025-12-02T21:14:39Z) - 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) - The Foundation Model Transparency Index [55.862805799199194]
The Foundation Model Transparency Index specifies 100 indicators that codify transparency for foundation models.
We score developers in relation to their practices for their flagship foundation model.
Overall, the Index establishes the level of transparency today to drive progress on foundation model governance.
arXiv Detail & Related papers (2023-10-19T17:39:02Z) - Extensible Consent Management Architectures for Data Trusts [0.0]
This paper proposes a framework for consent management in Data Trusts.
Data can flow across a network through "role tunnels" established based on corresponding legal capacities.
arXiv Detail & Related papers (2023-09-28T18:28:50Z) - Distributed Governance: a Principal-Agent Approach to Data Governance --
Part 1 Background & Core Definitions [0.0]
We provide a model to evolve Data governance toward Information governance.
This model bridges digital and non-digital information exchange.
We provide a framework to deploy a distributed governance model embedding checks and balance between human and technological governance.
arXiv Detail & Related papers (2023-08-14T17:12:07Z) - FedSOV: Federated Model Secure Ownership Verification with Unforgeable
Signature [60.99054146321459]
Federated learning allows multiple parties to collaborate in learning a global model without revealing private data.
We propose a cryptographic signature-based federated learning model ownership verification scheme named FedSOV.
arXiv Detail & Related papers (2023-05-10T12:10:02Z) - The Design and Implementation of a National AI Platform for Public
Healthcare in Italy: Implications for Semantics and Interoperability [62.997667081978825]
The Italian National Health Service is adopting Artificial Intelligence through its technical agencies.
Such a vast programme requires special care in formalising the knowledge domain.
Questions have been raised about the impact that AI could have on patients, practitioners, and health systems.
arXiv Detail & Related papers (2023-04-24T08:00:02Z) - Generative AI and the Digital Commons [0.0]
GFMs are trained on publicly available data and use public infrastructure.
We outline the risks posed by GFMs and why they are relevant to the digital commons.
We propose numerous governance-based solutions.
arXiv Detail & Related papers (2023-03-20T13:01:48Z) - Distributed Machine Learning and the Semblance of Trust [66.1227776348216]
Federated Learning (FL) allows the data owner to maintain data governance and perform model training locally without having to share their data.
FL and related techniques are often described as privacy-preserving.
We explain why this term is not appropriate and outline the risks associated with over-reliance on protocols that were not designed with formal definitions of privacy in mind.
arXiv Detail & Related papers (2021-12-21T08:44:05Z) - 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) - Limits of Individual Consent and Models of Distributed Consent in Online
Social Networks [1.0276024900942875]
A user who consents to allow access to their profile can expose the personal data of their network connections to non-consented access.
We introduce both a platform-specific model of "distributed consent" and a cross-platform model of a "consent passport"
In both models, individuals and groups can coordinate by giving consent conditional on that of their network connections.
arXiv Detail & Related papers (2020-06-29T16:00:11Z)
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.