Poster: Privacy-Preserving Compliance Checks on Ethereum via Selective Disclosure
- URL: http://arxiv.org/abs/2602.18539v1
- Date: Fri, 20 Feb 2026 14:54:22 GMT
- Title: Poster: Privacy-Preserving Compliance Checks on Ethereum via Selective Disclosure
- Authors: Supriya Khadka, Dhiman Goswami, Sanchari Das,
- Abstract summary: This work proposes a general Selective Disclosure Framework built on, designed to decouple attribute verification from identity revelation.<n>By utilizing client-side zk-SNARKs, the framework enables users to prove specific eligibility predicates without revealing underlying identity documents.<n>Preliminary results indicate that strict compliance requirements can be satisfied with negligible client-side latency.
- Score: 9.47737368469032
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Digital identity verification often forces a privacy trade-off, where users must disclose sensitive personal data to prove simple eligibility criteria. As blockchain applications integrate with regulated environments, this over-disclosure creates significant risks of data breaches and surveillance. This work proposes a general Selective Disclosure Framework built on Ethereum, designed to decouple attribute verification from identity revelation. By utilizing client-side zk-SNARKs, the framework enables users to prove specific eligibility predicates without revealing underlying identity documents. We present a case study, ZK-Compliance, which implements a functional Grant, Verify, Revoke lifecycle for age verification. Preliminary results indicate that strict compliance requirements can be satisfied with negligible client-side latency (< 200 ms) while preserving the pseudonymous nature of public blockchains.
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) - Confidential Wrapped Ethereum [0.0]
The proposal suggests creating a confidential version of wrapped (cWETH) fully within the application layer.<n>The solution combines the Elliptic Curve (EC) Twisted ElGamal-based commitment scheme to preserve confidentiality.<n>To enforce the correct generation of commitments, encryption, and decryption, zk-SNARKs are utilized.
arXiv Detail & Related papers (2025-07-12T10:00:50Z) - EGNInfoLeaker: Unveiling the Risks of Public Key Reuse and User Identity Leakage in Blockchain [10.349392384230274]
In this paper, we design a system called EGNInfoLeaker.<n>Our study is the first work that uncovers widespread public key reuse across peer-to-peer networks.<n>Going forward, our detection framework provides a foundation for enhancing real-world privacy preservation in decentralized networks.
arXiv Detail & Related papers (2025-07-02T12:07:03Z) - Privacy-Preserving Biometric Verification with Handwritten Random Digit String [49.77172854374479]
Handwriting verification has stood as a steadfast identity authentication method for decades.<n>However, this technique risks potential privacy breaches due to the inclusion of personal information in handwritten biometrics such as signatures.<n>We propose using the Random Digit String (RDS) for privacy-preserving handwriting verification.
arXiv Detail & Related papers (2025-03-17T03:47:25Z) - 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) - SD-BLS: Privacy Preserving Selective Disclosure of Verifiable Credentials with Unlinkable Threshold Revocation [0.0]
We propose a method for selective disclosure and privacy-preserving revocation of digital credentials.
We use second-order Elliptic Curves and Boneh-Lynn-Shacham (BLS) signatures.
Our system's unique design enables extremely fast revocation checks, even with large revocation lists.
arXiv Detail & Related papers (2024-06-27T09:41:13Z) - SeDe: Balancing Blockchain Privacy and Regulatory Compliance by Selective De-Anonymization [0.46040036610482665]
We propose a framework that balances privacy-preserving features by establishing a regulatory and compliant framework called Selective De-Anonymization (SeDe)<n>Our technique achieves this without leaving de-anonymization decisions or control in the hands of a single entity but distributing it among multiple entities while holding them accountable for their respective actions.
arXiv Detail & Related papers (2023-11-14T13:49:13Z) - 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) - FedGT: Identification of Malicious Clients in Federated Learning with Secure Aggregation [69.75513501757628]
FedGT is a novel framework for identifying malicious clients in federated learning with secure aggregation.
We show that FedGT significantly outperforms the private robust aggregation approach based on the geometric median recently proposed by Pillutla et al.
arXiv Detail & Related papers (2023-05-09T14:54:59Z) - A Randomized Approach for Tight Privacy Accounting [63.67296945525791]
We propose a new differential privacy paradigm called estimate-verify-release (EVR)
EVR paradigm first estimates the privacy parameter of a mechanism, then verifies whether it meets this guarantee, and finally releases the query output.
Our empirical evaluation shows the newly proposed EVR paradigm improves the utility-privacy tradeoff for privacy-preserving machine learning.
arXiv Detail & Related papers (2023-04-17T00:38:01Z) - Breaking the Communication-Privacy-Accuracy Tradeoff with
$f$-Differential Privacy [51.11280118806893]
We consider a federated data analytics problem in which a server coordinates the collaborative data analysis of multiple users with privacy concerns and limited communication capability.
We study the local differential privacy guarantees of discrete-valued mechanisms with finite output space through the lens of $f$-differential privacy (DP)
More specifically, we advance the existing literature by deriving tight $f$-DP guarantees for a variety of discrete-valued mechanisms.
arXiv Detail & Related papers (2023-02-19T16:58:53Z) - 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)
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.