COD-ssi: Enforcing Mutual Privacy for Credential Oblivious Disclosure in Self Sovereign Identity
- URL: http://arxiv.org/abs/2604.10685v1
- Date: Sun, 12 Apr 2026 15:23:06 GMT
- Title: COD-ssi: Enforcing Mutual Privacy for Credential Oblivious Disclosure in Self Sovereign Identity
- Abstract summary: Credential selective disclosure is one of the most attractive privacy-preserving features of Self-Sovereign Identity (SSI)<n>We introduce COD-ssi, a novel framework that leverages Oblivious Pseudorandom Functions to allow Verifiers to selectively access a subset of claims without revealing which specific claims were accessed to the credential Holder.<n>Results show that provable mutual privacy in the context of SSI can be achieved with just moderate computational and communication overhead.
- Score: 2.2813862690135607
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: The Self-Sovereign Identity (SSI) paradigm is instrumental for decentralised identity management, allowing an entity to create, manage, and present their digital credentials without relying on centralised authorities. Credential selective disclosure is one of the most attractive privacy-preserving features of SSI, allowing users to reveal only the minimum necessary information from their credentials. However, current selective disclosure mechanisms primarily focus on protecting the privacy of credential Holders, while offering limited protection to the Verifiers of credentials. Indeed, the specific credential information requested by a Verifier can inadvertently reveal to credential Holders sensitive information, including internal decision-making criteria, business rules, or strategic plans. In this work, we address this threat by proposing, to the best of our knowledge, the first approach that enforces mutual privacy in credential exchanges. To this end, we introduce COD-ssi (Claim Oblivious Disclosure for SSI), a novel framework that leverages Oblivious Pseudorandom Functions to allow Verifiers to selectively access a subset of claims without revealing which specific claims were accessed to the credential Holder. The security of our solution is formally verified and its feasibility is assessed through the experimental evaluation of our open-source prototype implementation. These results show that provable mutual privacy in the context of SSI can be achieved with just moderate computational and communication overhead.
Related papers
- Your Privacy Depends on Others: Collusion Vulnerabilities in Individual Differential Privacy [50.66105844449181]
Individual Differential Privacy (iDP) promises users control over their privacy, but this promise can be broken in practice.<n>We reveal a previously overlooked vulnerability in sampling-based iDP mechanisms.<n>We propose $(varepsilon_i,_i,overline)$-iDP a privacy contract that uses $$-divergences to provide users with a hard upper bound on their excess vulnerability.
arXiv Detail & Related papers (2026-01-19T10:26:12Z) - Achieving Flexible and Secure Authentication with Strong Privacy in Decentralized Networks [13.209703999398805]
IRAC is a flexible credential model that unifies credentials from heterogeneous issuers.<n>We design a secure decentralized revocation mechanism where holders prove non-revocation by demonstrating their credential's revocation within a gap in the issuer's sorted list.
arXiv Detail & Related papers (2025-12-23T10:49:05Z) - VoxGuard: Evaluating User and Attribute Privacy in Speech via Membership Inference Attacks [51.68795949691009]
We introduce VoxGuard, a framework grounded in differential privacy and membership inference.<n>For attributes, we show that simple transparent attacks recover gender and accent with near-perfect accuracy even after anonymization.<n>Our results demonstrate that EER substantially underestimates leakage, highlighting the need for low-FPR evaluation.
arXiv Detail & Related papers (2025-09-22T20:57:48Z) - Towards Privacy-Preserving Revocation of Verifiable Credentials with Time-Flexibility [0.36832029288386137]
Self-Sovereign Identity (SSI) is an emerging paradigm for authentication and credential presentation.<n>The EUDI Digital Identity wallet is about to become a concrete implementation of this paradigm.<n>We propose the basis of a novel method that customizes the use of anonymous hierarchical identity-based encryption.
arXiv Detail & Related papers (2025-03-27T21:58:32Z) - Distributed Identity for Zero Trust and Segmented Access Control: A Novel Approach to Securing Network Infrastructure [4.169915659794567]
This study assesses security improvements achieved when distributed identity is employed with ZTA principle.<n>The study suggests adopting distributed identities can enhance overall security postures by an order of magnitude.<n>The research recommends refining technical standards, expanding the use of distributed identity in practice, and its applications for the contemporary digital security landscape.
arXiv Detail & Related papers (2025-01-14T00:02:02Z) - Balancing Confidentiality and Transparency for Blockchain-based Process-Aware Information Systems [43.253676241213626]
We propose an architecture for blockchain-based PAISs to preserve confidentiality and transparency.<n>Smart contracts enact, enforce and store public interactions, while attribute-based encryption techniques are adopted to specify access grants to confidential information.<n>We assess the security of our solution through a systematic threat model analysis and evaluate its practical feasibility.
arXiv Detail & Related papers (2024-12-07T20:18:36Z) - Privacy-Enhanced Adaptive Authentication: User Profiling with Privacy Guarantees [0.6554326244334866]
This paper introduces a novel privacy-enhanced adaptive authentication protocol.<n>It dynamically adjusts authentication requirements based on real-time risk assessments.<n>By adhering to data protection regulations such as CCPA, our protocol not only enhances security but also fosters user trust.
arXiv Detail & Related papers (2024-10-27T19:11:33Z) - 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) - Unified Mechanism-Specific Amplification by Subsampling and Group Privacy Amplification [54.1447806347273]
Amplification by subsampling is one of the main primitives in machine learning with differential privacy.
We propose the first general framework for deriving mechanism-specific guarantees.
We analyze how subsampling affects the privacy of groups of multiple users.
arXiv Detail & Related papers (2024-03-07T19:36:05Z) - Statistical anonymity: Quantifying reidentification risks without
reidentifying users [4.103598036312231]
Data anonymization is an approach to privacy-preserving data release aimed at preventing participants reidentification.
Existing algorithms for enforcing $k$-anonymity in the released data assume that the curator performing the anonymization has complete access to the original data.
This paper explores ideas for reducing the trust that must be placed in the curator, while still maintaining a statistical notion of $k$-anonymity.
arXiv Detail & Related papers (2022-01-28T18:12:44Z) - Reinforcement Learning on Encrypted Data [58.39270571778521]
We present a preliminary, experimental study of how a DQN agent trained on encrypted states performs in environments with discrete and continuous state spaces.
Our results highlight that the agent is still capable of learning in small state spaces even in presence of non-deterministic encryption, but performance collapses in more complex environments.
arXiv Detail & Related papers (2021-09-16T21:59:37Z)
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.