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2,611 results · page 18 of 109
M. Badrul Alam, Shazneen Islam Akhi, A K M Mahbubur Rahman, Jubaida Nur Jumana · 7 authors
No abstract is available for this record.
Sudip Diyasi, Ankita Ghosh, Dipankar Dey
Fraud risks are on the rise with the increase in cryptocurrency transactions; the traditional detection methods become inadequate. This paper proposes a hybrid deep-learning framework for real-time fraud detection in Bitcoin transactions. Algorithms like Random Forest, support vector machine (SVM), Logistic Regression, and XGBoost are used to analyse transaction patterns and anomalies with a high level of accuracy. Different models have been tested in transaction data for Bitcoin, and the best-performing model was XGBoost with an accuracy of 96.94%. Advanced machine learning techniques enrich a system through secure data-driven insights and real-time anomaly detection, thus enhancing fraud risk detection. The obstacles faced are scalability, privacy issues, and inability to adjust models according to the evolving fraud technique. Future advancements might deal with federated learning, encryption methods, and cross-platform prevention to make the detection of fraud more secure. This paper indicates how well deep learning-based detection of fraud can scale and work efficiently to strengthen trust in digital financial systems.
Jolly Parikh, Ravgautam Aujla, Vikas Rai, Yugnanda Puri · 6 authors
No abstract is available for this record.
Ruonan Chen, Yang Zhang, Dawei Li, Yizhong Liu · 8 authors
Blockchain and cryptocurrencies are developing rapidly, and the scalability issue has become a constraint on their practical application and development. Off-chain payment channel is an effective solution to the scalability problem of blockchain. Currently, various payment channel protocols have been proposed. However, privacy issues are vital in payment channels. Existing works that consider privacy issues mainly focus on payment channel networks and payment channel hubs, while there is little work on two-party and multi-party channels. In addition, many existing payment channel works that consider privacy protection fix the transaction amounts to ensure the hiding of payment relationships or rely on smart contracts, which will hinder the practical application of payment channels. In this work, we propose a two-party privacy-preserving payment channel protocol that is compatible with Bitcoin (TBPChannel), achieving value privacy and unlinkability, while supporting variable transaction amounts. On this basis, we propose a privacy-preserving multi-party payment channel protocol (MBPChannel), which removes the role of untrusted operators in previous multi-party settings and further achieves robustness. We formally model the protocols in the universal composability framework and prove the security. Finally, we implement the protocols and provide a performance evaluation. The results demonstrate the scalability and practicality of our protocols. Compared to current protocols, even though we use privacy-preserving methods, our protocols are still efficient and applicable in practice.
Jonathan Bootle, Vadim Lyubashevsky, Antonio Merino-Gallardo
No abstract is available for this record.
Brugeres, Maxence, Languille, Victor, Kuznetsov, Petr, Zarfaoui, Hamza
We propose a decentralized asset-transfer system that enjoys full privacy: no party can learn the details of a transaction, except for its issuer and its recipient. Furthermore, the recipient is not aware of the sender’s identity. Our system does not rely on consensus or synchrony assumptions, and therefore, it is responsive, since it runs at the actual network speed. Under the hood, every transaction creates a consumable coin equipped with a non-interactive zero-knowledge proof (NIZK) that confirms that the issuer has sufficient funds without revealing any information about her identity, the recipient’s identity, or the payment amount. Moreover, we equip our system with a regulatory enforcement mechanism that can be used to regulate transfer limits or restrict specific addresses from sending or receiving funds, while preserving the system’s privacy guarantees. Finally, we report on PaxPay, our implementation of Fully Private Asset Transfer (FPAT) that uses the Gnark library for the NIZKs. In our benchmark, PaxPay exhibits better performance than earlier proposals that either ensure only partial privacy, require some kind of network synchrony or do not implement regulation features. Our system thus reconciles privacy, responsiveness, regulation enforcement and performance.
Mosbah Alown, Mehmet Sabır Kiraz, Muhammed Ali Bingöl
Electronic voting (e-voting) systems have significantly improved the traditional voting process by addressing key concerns such as security, public acceptability, and convenience. However, these systems often face unique challenges, such as ensuring voter privacy and verifiability, preventing coercion and double voting, and maintaining scalability while protecting participant confidentiality. This study critically analyses and compares various e-voting schemes and technologies, evaluating their security features, verifiability mechanisms, and potential vulnerabilities. This paper reviews Direct Recording Electronic (DRE) voting, internet voting, and blockchain-based e-voting systems. In so doing, we provide an understanding of cryptographic primitives employed in e-voting systems and how they address specific characteristics and challenges associated with each voting scheme. Furthermore, we examine the applications proposed by previous studies in the context of these voting systems, assessing their strengths, limitations, and impact on democratic procedures. The cryptographic primitives reviewed include techniques like homomorphic encryption, blind signatures, and zero-knowledge proofs, which can enhance voter privacy, verifiability, and resistance to coercion and double voting.
Mridul Kumar Gupta, Mradul Singh Tawar, Mrityunjay Kumar
No abstract is available for this record.
S. Revathi, V. R. Elangovan
No abstract is available for this record.
István András Seres, Noemi Glaeser, Joseph Bonneau
No abstract is available for this record.
Piyush Kumar Ghosh, Aditya Bhushan, Dharmendra Kumar, Ashutosh Kumar Singh
No abstract is available for this record.
Himanshu Nandanwar, Rahul Katarya
No abstract is available for this record.
Edward Danso Ansong, Simon Bonsu Osei, Raphael Adjetey Adjei
The surge in identity fraud, driven by the rapid adoption of mobile money, internet banking, and e-services during the COVID-19 pandemic, underscores the need for robust cybersecurity solutions. Zero-Knowledge Proofs (ZKPs) e... | Find, read and cite all the research you need on Tech Science Press
Piyush Kumar Ghosh, Aditya Bhushan, Dharmendra Kumar, Ashutosh Kumar Singh
No abstract is available for this record.
Natasha Wanjari, Pratiksha Chafle, Rahul Moriwal
No abstract is available for this record.
Zubaida Rehman, Iqbal Gondal, Hai Dong, Mengmeng Ge · 6 authors
Eclipse attacks, which isolate victim nodes by monopolizing their peer connections, remain a critical threat to Ethereum’s consensus mechanism. To address this, we present a principled framework for detecting Eclipse attacks in Ethereum peer-to-peer networks, grounded in a formal adversarial model. Existing defenses are either ad-hoc or lack provable guarantees, leaving open questions about their reliability under adaptive adversaries. Our work aims to bridge this gap by formally defining eclipse attack detection as a security property. We specify soundness, completeness, and robustness theorems under bounded adversarial drift, and derive formal guarantees within false positive and false negative bounds, resilience to adversarial manipulation, and multi-node compositional reliability. We then instantiate a lightweight detection framework that maps packet-level traffic features to predictions using ensemble classifiers (Random Forest, XGBoost). The system was validated using a controlled Ethereum testbed and extended with CTGAN-generated synthetic traces to emulate networks of up to 100 nodes. Empirical evaluation shows that our framework achieves up to 96% F1-score with sub-second inference latency, well within Ethereum’s 12-second Proof-of-Stake validator time slots. These findings demonstrate that lightweight statistical features, when coupled with formal analysis, enable accurate, efficient, and scalable detection of network-level partitioning attacks. Our work establishes a deployable and theoretically grounded defense foundation for securing modern blockchain systems against eclipse adversaries.
Samuel Polgar
Digital credential wallets manage identity documents such as government IDs and financial certificates, face the trilemma of privacy, security, and usability. Optimizing for anonymity by using Anonymous Credentials enhances privacy, but introduces challenges. Current benchmarks show verification using zero-knowledge proofs of knowledge taking 50–500ms, far exceeding the <1ms of standard credentials, impeding usability. Additionally, anonymity complicates security: preventing multiple-credential issuance (sybil resistance) or enforcing revocation becomes difficult when both users and objects are essentially secret. These issues are urgent due to the EU’s 2026 mandate for EU-wide credential wallet usage, which will drive widespread adoption of digital credential wallets, while critical use cases, like privately combining credentials from multiple issuers for KYC, emphasize the importance of this work. This thesis extends existing work and develops new, fast cryptographic primitives for privacy preserving credential wallets. It introduces the fastest anonymous credential scheme with a 3.77ms Show+Verify time for 10 attributes, outperforming prior methods by 10-15%. Three extensions enhance this scheme. 1) formalized Identity Binding property for secure multi-issuer, multi-credential verification, with an implementation verifying 16 credentials from unique issuers in 72ms; 2) new nullifier constructions using Σ-protocols without pairings, improving privacy-preserving sybil resistance by 5x over previous approaches; 3) T-SIRIS, a threshold-issued, sybil-resistant identity system with near-constant Show+Verify times, over 30x faster than comparable systems [RAR+24]. These advancements are validated by an open-source Rust benchmarking library, delivering standardized empirical data across anonymous credential schemes.
Jacob Gorman, Lucjan Hanzlik, Aniket Kate, Pratyay Mukherjee · 6 authors
Web3 applications, such as on-chain gaming, require unbiased and publicly verifiable randomness that can be obtained quickly and cost-effectively whenever needed. Existing services, such as those based on Verifiable Random Functions (VRF), incur network delays and high fees due to their highly interactive nature. FlexiRand [CCS 2023] addressed these problems by hiding the output of the VRF and using that as a seed to derive many randomnesses locally. These randomnesses are instantly available for usage. However, these randomnesses can not be verified independently (or instantly) without disclosing the seed, leaving scope for malicious actors to cheat. To solve this problem, we introduce a new notion, called instantly-verifiable VRF (iVRF), which enables the generation of many randomnesses from one VRF output seed, such that each of them is verifiable independently - this enables the solution to generate randomnesses, such that they are and also . To instantiate we propose a generic construction called InstaRand - it combines any (possibly distributed) VRF at the server's end with another VRF at the client's end to construct an iVRF. Our specific instantiation uses the BLS-based GLOW-DVRF [Euro S&P 2021] at the server's end and the DDH-based VRF of Goldberg et al. [RFC 2023] at the client's end. We use the universal composability framework to analyze the security. Moreover, due to its generality, InstaRand can be instantiated with any post-quantum secure VRF to yield a post-quantum secure iVRF. Our experiments demonstrate that our instantiation of InstaRand is . The client incurs a cost to generate the seed (server's VRF output) by querying the GLOW-dVRF servers once. Once the seed is set up, the client locally generates the pseudorandom value on demand in , avoiding the client-server round-trip delay. Each value can be independently verified in . This yields a improvement in terms of output generation and improvement in verification cost over existing solutions.
Christos Karapapas, Iakovos Pittaras, George C. Polyzos, Constantinos Patsakis
The InterPlanetary File System~(IPFS) offers a decentralized approach to file storage and sharing, promising resilience and efficiency while also realizing the Web3 paradigm. Simultaneously, the offered anonymity raises significant questions about potential misuse. In this study, we explore methods that malicious actors can exploit IPFS to upload and disseminate harmful content while remaining anonymous. We evaluate the role of pinning services and public gateways, identifying their capabilities and limitations in maintaining content availability. Using scripts, we systematically test the behavior of these services by uploading malicious files. Our analysis reveals that pinning services and public gateways lack mechanisms to assess or restrict the propagation of malicious content.
Ke Ding, Xiaoyan Hu, Zhuozhuo Shu, Guang Cheng · 6 authors
With the development of the blockchain economy, many criminals are attempting to profit from Non-Fungible Tokens (NFTs). Under the cover of encrypted tunnels, efficiently identifying NFT application traffic has become a challenge. Moreover, NFT applications exhibit a high degree of similarity in communication interfaces, traffic encryption settings, and even business behaviors, making it difficult to distinguish the traffic of different NFT applications. The fact that a regulator can determine the users of a website’s visit by fingerprinting encrypted traffic patterns brings hope for solving this challenge. However, existing Deep Learning (DL) fingerprinting requires complete flows for identification, leading to high training costs and identification latency. This paper presents a novel NFT application fingerprinting method over encrypted tunnels called NFT-AF (NFT Application Fingerprinting). It extracts encrypted tunnel, statistical, and sequential features from early-stage network traffic to generate multi-dimensional fingerprints. Then, it uses a lightweight random forest as the classifier to efficiently identify NFT applications. Our experimental results demonstrate that NFT-AF effectively identifies NFT applications over encrypted tunnels using early-stage session packets and outperforms the state-of-the-art (SOTA) methods.
Hamza Baniata, Giovanny Eduardo Caluña Chicaiza
Election is the key process typically utilized for maintaining democracy in a given society. Recent technological advancements, such as Blockchain (BC), have been already deployed in previous works to realize non-conventional e-Voting systems. The main goal for such proposals is to provide the necessary level of security and reliability, while maintaining transparency, trust, and remote elections. However, the distributed and publicity nature of BC brought new challenges related to privacy and performance trade-off. This paper aims to address these issues by integrating smart contracts for reliability and transparency, Differential Privacy for enhancing vote anonymity, and Self-Sovereign Identities to unlock the potential of the Web3 framework for verifiable credentials and digital identities. Specifically, a novel ($k,\epsilon $)-differential privacy mechanism is developed, where a randomly selected candidate is pivoted from which retrievable votes are transferred to other candidates. Final election results are then statistically approximated. We evaluate the proposed methods for different arrival rates (10–80 TX/s), different total numbers of cast votes (10k–50k votes), and different numbers of elected candidates (2–8 candidates). To demonstrate the applicability of our proposal in real-life scenarios, we deploy our SC on a cloud-based permissioned BC network using Hyperledger Besu, with nodes set in Google’s EU and USA data centers. Our experimental results showed that BP-Vot could provide 24% enhancement in latency over state-of-the-art solutions ($\approx 1$s/TX compared to 1.24 s/TX). Additionally, using a standardized Min-Max regression mechanism, we show that BP-Vot could provide no less than 98% accuracy in votes approximation during all experiments, with a linearly increasing accuracy trend as a function of the total number of cast votes. Finally, we formally evaluate the proposed differential privacy method and prove that it is robust against reconstruction attacks.
Marcela Lupu, Iulian Aciobăniţei
No abstract is available for this record.
Funmi Eko Ezeh, Stephanie Onyekachi Oparah, Pamela Gado, Stephen Vure Gbaraba · 5 authors
The growing threat posed by quantum computing to traditional cryptographic systems demands a radical redesign of digital voting architectures. This review explores the development of a post-quantum blockchain voting protocol, emphasizing the integration of zero-knowledge proofs (ZKPs) to ensure data privacy, voter anonymity, and verifiable election integrity. The study synthesizes advancements in lattice-based and hash-based cryptographic algorithms capable of withstanding quantum attacks and evaluates their applicability within decentralized ledger frameworks. Particular attention is paid to the role of ZKPs—such as zk-SNARKs and zk-STARKs—in constructing tamper-resilient, end-to-end verifiable voting systems without compromising performance or transparency. Through critical analysis of recent protocols, consensus mechanisms, and deployment models, this paper identifies key design principles for scalable, secure, and inclusive e-voting infrastructures. The review concludes with strategic recommendations for transitioning from prototype systems to robust electoral frameworks in anticipation of the quantum era.
Christian Delgado‐von‐Eitzen, Manuel J. Fernández Iglesias, Luis Anido, Martín Llamas Nistal
This paper introduces a novel access control architecture based on a dual-blockchain model that separates access management from data storage to enhance security and scalability. The system enables users to submit access requests to a primary blockchain, where smart contracts dynamically verify permissions before retrieving data from a secondary, isolated blockchain. This design enforces fine-grained, account-level access control while preventing direct exposure of sensitive data. A proof of concept was implemented using Hyperledger YUI to interconnect Ethereum-based blockchains, demonstrating secure inter-chain communication and dynamic permission enforcement. The proposed solution addresses key limitations in existing blockchain infrastructures and offers a flexible, decentralized framework suitable for applications requiring robust data governance and regulatory compliance.