10 research outputs found
Incorporating Zero-Knowledge Succinct Non-interactive Argument of Knowledge for Blockchain-based Identity Management with off-chain computations
In today's world, secure and efficient biometric authentication is of keen
importance. Traditional authentication methods are no longer considered
reliable due to their susceptibility to cyber-attacks. Biometric
authentication, particularly fingerprint authentication, has emerged as a
promising alternative, but it raises concerns about the storage and use of
biometric data, as well as centralized storage, which could make it vulnerable
to cyber-attacks. In this paper, a novel blockchain-based fingerprint
authentication system is proposed that integrates zk-SNARKs, which are
zero-knowledge proofs that enable secure and efficient authentication without
revealing sensitive biometric information. A KNN-based approach on the FVC2002,
FVC2004 and FVC2006 datasets is used to generate a cancelable template for
secure, faster, and robust biometric registration and authentication which is
stored using the Interplanetary File System. The proposed approach provides an
average accuracy of 99.01%, 98.97% and 98.52% over the FVC2002, FVC2004 and
FVC2006 datasets respectively for fingerprint authentication. Incorporation of
zk-SNARK facilitates smaller proof size. Overall, the proposed method has the
potential to provide a secure and efficient solution for blockchain-based
identity management
Explainable AI (XAI): core ideas, techniques and solutions
As our dependence on intelligent machines continues to grow, so does the demand for more transparent and interpretable models. In addition, the ability to explain the model generally is now the gold standard for building trust and deployment of Artificial Intelligence (AI) systems in critical domains. Explainable Artificial Intelligence (XAI) aims to provide a suite of machine learning (ML) techniques that enable human users to understand, appropriately trust, and produce more explainable models. Selecting an appropriate approach for building an XAI-enabled application requires a clear understanding of the core ideas within XAI and the associated programming frameworks. We survey state-of-the-art programming techniques for XAI and present the different phases of XAI in a typical ML development process. We classify the various XAI approaches and using this taxonomy, discuss the key differences among the existing XAI techniques. Furthermore, concrete examples are used to describe these techniques that are mapped to programming frameworks and software toolkits. It is the intention that this survey will help stakeholders in selecting the appropriate approaches, programming frameworks, and software toolkits by comparing them through the lens of the presented taxonomy