Related papers: ZKlaims: Privacy-preserving Attribute-based Creden…

Privacy and security are often intertwined. For example, identity theft is rampant because we have become accustomed to authentication by identification. To obtain some service, we provide enough information about our identity for an…

The amount of information generated grows as more and more sensor and IoT devices are deployed in smart cities. It is of utmost importance for us to consider the privacy data leakage and compromised identity from both outside adversaries…

This paper presents a new method for quantum identity authentication (QIA) protocols. The logic of classical zero-knowledge proofs (ZKPs) due to Schnorr is applied in quantum circuits and algorithms. This novel approach gives an exact way…

Machine unlearning aims to remove the influence of specific data points from a trained model to satisfy privacy, copyright, and safety requirements. In real deployments, providers distribute a global model to many edge devices, where each…

Anonymous Single-Sign-On authentication schemes have been proposed to allow users to access a service protected by a verifier without revealing their identity which has become more important due to the introduction of strong privacy…

The computing continuum introduces new challenges for access control due to its dynamic, distributed, and heterogeneous nature. In this paper, we propose a Zero-Trust (ZT) access control solution that leverages decentralized identification…

Blockchain-based Attribute-Based Access Control (BC-ABAC) offers a decentralized paradigm for secure data governance but faces two inherent challenges: the transparency of blockchain ledgers threatens user privacy by enabling…

Zero-Knowledge Proofs (ZKPs) have emerged as a powerful tool for secure and privacy-preserving computation. ZKPs enable one party to convince another of a statement's validity without revealing anything else. This capability has profound…

We introduce a formal model for order queries on lists in zero knowledge in the traditional authenticated data structure model. We call this model Privacy-Preserving Authenticated List (PPAL). In this model, the queries are performed on the…

Systems managing Verifiable Credentials are becoming increasingly popular. Unfortunately, their support for revoking previously issued credentials allows verifiers to effectively monitor the validity of the credentials, which is sensitive…

When users query proprietary LLM APIs, they receive outputs with no cryptographic assurance that the claimed model was actually used. Service providers could substitute cheaper models, apply aggressive quantization, or return cached…

With the rise of machine learning techniques, ensuring the fairness of decisions made by machine learning algorithms has become of great importance in critical applications. However, measuring fairness often requires full access to the…

Privacy-preserving blockchain systems are essential for protecting transaction data, yet they must also provide auditability that enables auditors to recover participant identities and transaction amounts when warranted. Existing designs…

Verification of the integrity of deep learning inference is crucial for understanding whether a model is being applied correctly. However, such verification typically requires access to model weights and (potentially sensitive or private)…

This paper introduces the zk-IoT framework, a novel approach to enhancing the security of Internet of Things (IoT) ecosystems through the use of Zero-Knowledge Proofs (ZKPs) on blockchain platforms. Our framework ensures the integrity of…

This paper introduces a new set of privacy-preserving mechanisms for verifying compliance with location-based policies for vehicle taxation, or for (electric) vehicle (EV) subsidies, using Zero-Knowledge Proofs (ZKPs). We present the design…

Zero-Knowledge Proofs (ZKP) are protocols which construct cryptographic proofs to demonstrate knowledge of a secret input in a computation without revealing any information about the secret. ZKPs enable novel applications in private and…

Ensuring that AI models are both verifiable and privacy-preserving is important for trust, accountability, and compliance. To address these concerns, recent research has focused on developing zero-knowledge machine learning (zkML)…

Privacy-preserving technologies have introduced a paradigm shift that allows for realizable secure computing in real-world systems. The significant barrier to the practical adoption of these primitives is the computational and communication…

With the rapid development of Zero-Knowledge Proofs (ZKPs), particularly Succinct Non-Interactive Arguments of Knowledge (SNARKs), benchmarking various ZK tools has become a valuable task. ZK-friendly hash functions, as key algorithms in…