Speaker anonymization protects against speaker identity inference, yet third parties cannot verify that released speech is authenticated and anonymized as predefined without revealing the original. We propose Verifiable Speaker Anonymization (VSA), a paradigm that enables public verification that a predefined anonymization has been applied while the original remains hidden. We instantiate this paradigm as ZK-VSA using zero-knowledge succinct non-interactive arguments of knowledge (ZK-SNARKs): we encode phase vocoder with time-scale modification (PV-TSM) as arithmetic constraints suitable for succinct proofs, complemented by SNARK-friendly phase handling, and integrate cryptographic commitments with digital signatures for authentication. We evaluate ZK-VSA on LibriSpeech, using automatic speech recognition (ASR) for intelligibility and automatic speaker verification (ASV) for anonymity. Our proof-constrained anonymization closely matches floating-point PV-TSM, while proofs add only a slight overhead and verify in milliseconds. These results demonstrate the practicality of VSA and open a path to proof-based guarantees for broader speech transformations.
Identity-based cryptography (IBC), proposed by Adi Shamir, revolutionized public key authentication by eliminating the need for certificates, enabling a more efficient and scalable approach to cryptographic systems. Meanwhile, in \cite{Katsumata2024group}, Katsumata et al. were the first to present the blind signature protocol based on the hardness assumption of isogeny with provable security, which resembles the Schnorr blind signature. Building upon these foundational concepts, we propose an Identity-Based Blind Signature Scheme with an Honest Zero-Knowledge Verifier utilizing the CSIDH framework. This scheme combines blind signatures for privacy preservation with zero-knowledge proofs to ensure the verifier's honesty without revealing any additional information. Leveraging the quantum-resistant properties of CSIDH, a post-quantum secure scheme based on supersingular isogenies, our scheme offers strong protection against quantum adversaries while maintaining computational efficiency. We analyze the security of the introduced protocol in the standard cryptographic model and demonstrate its effectiveness in safeguarding privacy and verifier honesty. Furthermore, we present a performance evaluation, confirming the practical viability of this quantum-resistant cryptographic solution for privacy-preserving applications. This work advances the creation of secure, and scalable cryptographic systems for the post-quantum era.
The advent of decentralized finance has ushered in a transformative era in the financial sector, leveraging blockchain technology to facilitate peer-to-peer transactions without traditional intermediaries. Amidst this innovation, the DeFi landscape faces the pervasive threat of rugpulls, where developers abruptly abandon projects post-fundraising, leaving investors with devalued assets. This growing concern highlights a critical research gap in the proactive detection and prevention of such fraudulent schemes. To combat this, we propose RUGSCREENER, a temporal graph neural network-based solution to identify rugpull risks within DeFi transactions. It employs a dynamic representation of blockchain interactions, enriched with comprehensive node attributes and effective temporal graph learning techniques based on memory and attention mechanisms, effectively capturing the rapid-moving and complex transaction patterns indicative of potential fraud. Our evaluation is based on a newly compiled Ethereum dataset that includes two subsets: an unlabeled set with 1,882,114 transactions from 29,595 tokens for temporal graph representation learning, and a labeled set with 128,819 transactions from 1,000 tokens (500 rugpull and 500 benign) for downstream evaluation. Using this dataset, RUGSCREENER achieves a balanced accuracy of 95.7% in detecting rugpull tokens. Our extensive evaluation, utilizing the Ethereum dataset comprising 1000 tokens, showcases its robust performance with a balanced accuracy of 95.7% in detecting rugpull tokens. Remarkably, RUGSCREENER surpasses existing state-of-the-art graph learning models in detecting rugpull tokens with enhanced accuracy and reliability.
Pavel Pantiukhov, Dmitrii Koriakov, Tatiana V. Petrova, Jeovane Honório Alves · 6 authors
Blockchain technology has consistently attracted attention for its transparency, decentralization, and security. More everyday users, without technical expertise, are now using blockchain and cryptocurrencies to store funds. As decentralized finance (DeFi) rises, security and privacy challenges have emerged, particularly the risk of losing funds due to leaked private keys. Consequently, enhancing security with user-friendly authentication methods is crucial. Voice authentication is a promising solution to add a security layer to the blockchain, but it is difficult to implement in DeFi without compromising decentralization and data confidentiality. Furthermore, advances in deep learning in voice cloning pose risks to voice-based systems. To address these issues, we propose the ZK Verify Voice Authentication System, which enables spoofing-aware speaker verification for the XRP Ledger (XRPL). Confidentiality is ensured by integrating voice embeddings with zero-knowledge proofs (zk-SNARKs). Voice embeddings serve as digital signatures, with only their hash stored on XRPL. Our system improves security and user experience by allowing individuals to prove their identity without exposing voice data. This approach provides robust security and privacy for DeFi participants, while remaining accessible to those without technical expertise.
Wong Jie Sheng, Intan Farahana Kasmin, Salmiah Mohamad Amin, Nur Khairunnisha Zainal
Multi-Factor Authentication (MFA) has been gaining popularity in recent years for offering extra layers of protection to secure user accounts. Most of the integration of MFA today includes a OTP to be sent through a Short Message Service (SMS) identified by a user’s phone number or through an MFA application. However, MFA executions like these still possess underlying vulnerabilities like phishing attacks. The aim of this research is to propose an effective Software Engineering solution to decrease the number of successful attacks on user accounts during the MFA process. The proposed system suggests a first-level authentication in the form of textbox for users to input their current location of access. This can prompt the user naturally to always pay attention to the user information provided before they continue on with the MFA process. This can help users to identify a threat and realise an attempted phishing attack before giving the full access to the attackers if proceeded with the MFA process. This paper proposes a solution that takes in voice recognition biometric alongside the traditional OTP. An improved distributed ledger technology in the form of Hedera Hashgraph is also proposed which addresses efficiency and security shortcomings of Blockchains like the Ethereum Blockchain. For this research, self-selection sampling was carried out and 5 people were chosen to attend a one-to-one online interview session.