Smart city infrastructures are resulting in a concern regarding proper security and fine- grained access control for the access of the sensitive data generated by the interconnected devices and services. Having rich yet heterogeneous environments is a common scenario for organizations nowadays, and conventional access control mechanisms often do not suffice to provide support for flexible, scalable and decentralized authorization. Here, we develop a new access control method that leverages Cipher text-Policy Attribute-Based Encryption (CP-ABE) and Ethereum smart contracts to realize decentralized and fine-grained data access in smart city applications. Users are allowed to access and decrypt information only if they own a suitable set of attribute. They leverage smart contracts on the Ethereum blockchain to perform attribute verification, issue keys, and log access actions, which removes the need to trust a centralized authority. We also describe the architecture of the system, the design of smart contracts, and an implementation prototype. The experimental results can validate the computational efficiency, the gas consumption and the policy expressiveness of our approach, showing that it is a promising solution for secure and transparent data access control model in smart cities.
Xenia Hofmeier, Andrea Raguso, Ralf Sasse, Dennis Jackson · 5 authors
Aggregate signatures are digital signatures that compress multiple signatures from different parties into a single signature, thereby reducing storage and bandwidth requirements. BLS aggregate signatures are a popular kind of aggregate signature, deployed by Ethereum, Dfinity, and Cloudflare amongst others, currently undergoing standardization at the IETF. However, BLS aggregate signatures are difficult to use correctly, with nuanced requirements that must be carefully handled by protocol developers. In this work, we design the first models of aggregate signatures that enable formal verification tools, such as Tamarin and ProVerif, to be applied to protocols using these signatures. We introduce general models that are based on the cryptographic security definition of generic aggregate signatures, allowing the attacker to exploit protocols where the security requirements are not satisfied. We also introduce a second family of models formalizing BLS aggregate signatures in particular. We demonstrate our approach's practical relevance by modelling and analyzing in Tamarin a device attestation protocol called SANA. Despite SANA's claimed correctness proof, with Tamarin we uncover undocumented assumptions that, when omitted, lead to attacks.
Web3 applications, such as on-chain games, NFT minting, and leader elections necessitate access to unbiased, unpredictable, and publicly verifiable randomness. Despite its broad use cases and huge demand, there is a notable absence of comprehensive treatments of on-chain verifiable randomness services. To bridge this, we offer an extensive formal analysis of on-chain verifiable randomness services. We present the first formalization of on-chain verifiable randomness in the blockchain setting by introducing the notion of Verifiable Randomness as a Service (VRaaS). We formally define VRaaS using an ideal functionality$\mathcal{F}\text{VRaaS}$in the Universal Composability model. Our definition not only captures the core features of randomness services, such as unbiasability, unpredictability, and public verifiability, but also accounts for many other crucial nuances pertaining to different entities involved, such as smart contracts. Within our framework we study a generic design of Verifiable Random Function (VRF)-based randomness service - where the randomness requester provides an input on which the randomness is evaluated as VRF output. We show that it does satisfy our formal VRaaS definition. Furthermore, we show that the generic protocol captures many real-world randomness services like Chainlink VRF and Supra dVRF. Moreover, we investigate the minimalism of the frame-work. Towards that, first we show that, the two transactions in-built in our framework are actually necessary for any randomness service to support the essential qualities. We also discover practical vulnerabilities in other designs such as Algorand beacon, Pyth VRF and Band VRF, captured within our framework.
As global financial ecosystems become increasingly digitized, the need for secure, resilient, and interoperable frameworks to protect cross-border transactions and digital financial identities has grown exponentially.Traditional perimeter-based security models have proven insufficient in addressing the sophisticated cyber threats targeting financial networks, especially in decentralized and multi-jurisdictional environments.This has spurred the adoption of Zero Trust Architectures (ZTA)-a paradigm that assumes no implicit trust across networks, devices, or users-and mandates continuous verification at every interaction point.While ZTA enhances access control and minimizes attack surfaces, it faces implementation challenges in distributed financial infrastructures due to trust management, data integrity, and auditability concerns.Simultaneously, blockchain protocols-with their decentralized consensus, immutability, and cryptographic assurance-have emerged as powerful enablers of secure, transparent, and tamperresistant financial systems.This article explores the convergence of ZTA and blockchain technologies as a transformative strategy for enhancing the confidentiality, integrity, and availability of cross-border payment systems and digital identity frameworks.It examines how smart contracts, decentralized identifiers (DIDs), and distributed ledgers can reinforce ZTA principles such as least-privilege access, continuous authentication, and micro-segmentation in a decentralized context.Drawing on real-world use cases and regulatory insights, the study proposes a layered security model integrating ZTA with permissioned blockchain infrastructures, highlighting architectural synergies, potential threats, and scalability considerations.It also addresses the interoperability challenges and governance frameworks necessary for adoption in multi-stakeholder financial environments.By bridging trustless identity verification with cryptographic consensus, this integrated approach offers a future-ready blueprint for securing global digital finance in the era of open banking, fintech innovation, and evolving cyber threats.
With the shifting from traditional grids to smart grids, there is an immense shift towards decentralized energy trading wherein “prosumers” can enter peer-to-peer transactions. This model decreases dependence on centralized utilities and maximizes efficient, flexible, and resilient energy distribution. It enhances transparency and trust by automating and securing trades via smart contracts. No intermediaries are required; hence transaction costs are low. However, an attack that would breach the security guarantees of blockchain systems-by tremendous quantum computers-might break a few of the older cryptographic methods or even reveal very significant portions of their keys. This paper introduces a blockchain-based decentralized framework for energy trading in smart grids, with a strong emphasis on post-quantum cryptography to safeguard transactions against quantum threats. We explore post-quantum cryptographic techniques, particularly lattice-based algorithms due to its compact signature sizes and strong security capability for the future-proof blockchain enabled smart grids. The proposed system model ensures secure and decentralized energy trading while incorporating off-chain signature validation to enhance computational efficiency. Unlike previous studies that primarily focus on market structure or consensus protocols, this work introduces a quantum-resilient architecture with an off-chain transaction validation mechanism, enabling high-throughput trading secured against future cryptographic vulnerabilities. The novel feature of the proposed model is the integration of off-chain post-quantum cryptographic verification into a blockchain energy trading architecture that is practically deployable on embedded hardware. Compared to existing solutions, the proposed method ensures quantum-resilient authentication while reducing gas costs by 40% and improving computational efficiency achieving a signing time of 0.327 ms and verification time of 0.127 ms. The proposed framework represents a significant step toward future-proofing blockchain-enabled smart grids while maintaining performance, transparency, and resilience. The outcome of this research work presents a quantum-safe solution that strengthens the resilience of smart grid operations and ensures the security of decentralized energy trading.
Jonas Ballweg, Amir Kafshdar Goharshady, Zhaorun Lin
We consider the classical problem of running a decentralized and trustless auction, using a smart contract, on a programmable block-chain such as Ethereum. In our setting, there are n bidders who have paid a deposit to join the protocol. Each bidder i can make a bid 1 ≤ bi ≤ m and our goal is to find the highest bid (maxi bi) and its corresponding bidder (argmaxi bi) in a publicly-verifiable manner. Each bidder must be unaware of others' bids when making their own and should not be able to change their bid after having committed to it. Additionally, and most importantly, we aim to provide privacy to the losing bidders, ensuring that their bids remain undisclosed. This is particularly crucial in use-cases with repeated auctions in which knowledge of the bids in the previous auctions can affect the bidders' strategies. Formally, the information gained by any observer, whether a participant in the protocol or not, should precisely consist of the winning bid and its bidder and nothing more. We present a novel yet simple protocol for private sealed-bid auctions on the blockchain. Our protocol is decentralized and trustless. It is also both time- and gas-efficient. Our approach takes O(log m) time and costs O(log m) units of gas for each bidder. It also guarantees observational determinism with respect to all losing bids.
Rakesh Reddy Charla, Kalpan Dharamshi, Raj Sonani, Aishwarya Rajkumar Shah
Exponential increase in data in distributed retail and financial settings poses severe challenges to privacy of user’s preservation without compromising the effectiveness of AI-driven insights. Such sensitive fields are usually not easy to keep up with using the conventional centralized data engineering approaches that fail to comply with the regulatory, scalability, and latency constraints. This paper presents a brand-new federated data engineering framework with a focus on privacy-aware AI applications, based on the patterns seen in the real-world retail and finance workflows. The proposed framework benefits from federated learning paradigms, decentralized feature engineering, and privacy-preserving transformation techniques to offer secure, cross-node training of the modelling powered with overcoming data ownership and sector-specific regulation restrictions. The study describes the details of the key system components such as federated schema harmonization, edge-level preprocessing, and secure aggregation mechanisms. Performance evaluations performed on synthetic retail transaction data and federated credit risk datasets show that the framework strikes a balanced performance between privacy guarantees, data utility and computational efficiency. According to the results, the approach to federated data engineering provides an expandable and legally friendly way for implementing AI solutions for data-sensitive fields, which allows creating robust, data-protective intelligence systems for various industries.
In trans-border data (data transferred or accessed across national jurisdictions) exchange scenarios, identity authentication mechanisms serve as critical components for ensuring data security and privacy protection, with their effectiveness directly impacting the compliance and reliability of transnational operations. However, existing identity authentication systems face multiple challenges in trans-border contexts. Firstly, the transnational transfer of identity data struggles to meet the varying data-compliance requirements across different jurisdictions. Secondly, centralized authentication architectures exhibit vulnerabilities in trust chains, where single points of failure may lead to systemic risks. Thirdly, the inefficiency of certificate verification in traditional Public Key Infrastructure (PKI) systems fails to meet the real-time response demands of globalized business operations. These limitations severely constrain real-time identity verification in international business scenarios. To address these issues, this study proposes a trans-border distributed certificate-free identity authentication framework (STALE). The methodology adopts three key innovations. Firstly, it utilizes email addresses as unique user identifiers combined with a Certificateless Public Key Cryptography (CL-PKC) system for key distribution, eliminating both single-point dependency on traditional Certificate Authorities (CAs) and the key escrow issues inherent in Identity-Based Cryptography (IBC). Secondly, an enhanced Elliptic Curve Diffie–Hellman (ECDH) key-exchange protocol is introduced, employing forward-secure session key negotiation to significantly improve communication security in trans-border network environments. Finally, a distributed identity ledger is implemented, using the FISCO BCOS blockchain, enabling decentralized storage and verification of identity information while ensuring data immutability, full traceability, and General Data Protection Regulation (GDPR) compliance. Our experimental results demonstrate that the proposed method exhibits significant advantages in authentication efficiency, communication overhead, and computational cost compared to existing solutions.
Buy Now Pay Later (BNPL) is a rapidly proliferating e-commerce model, offering consumers to get the product immediately and defer payments. Meanwhile, emerging blockchain technologies endow BNPL platforms with digital currency transactions, allowing BNPL platforms to integrate with digital wallets. However, the transparency of transactions causes critical privacy concerns because malicious participants may derive consumers' financial statuses from on-chain asynchronous payments. Furthermore, the newly created transactions for deferred payments introduce additional time overheads, which weaken the scalability of BNPL services. To address these issues, we propose an efficient and privacy-preserving blockchain-based asynchronous payment scheme (Epass), which has promising scalability while protecting the privacy of on-chain consumer transactions. Specifically, Epass leverages locally verifiable signatures to guarantee the privacy of consumer transactions against malicious acts. Then, a privacy-preserving asynchronous payment scheme can be further constructed by leveraging time-release encryption to control trapdoors of redactable blockchain, reducing time overheads by modifying transactions for deferred payment. We give formal definitions and security models, generic structures, and formal proofs for Epass. Extensive comparisons and experimental analysis show that \textsf{Epass} achieves KB-level communication costs, and reduces time overhead by more than four times in comparisons with locally verifiable signatures and Go-Ethereum private test networks.
The purpose of the covert communication scheme is to conceal the communication behavior entirely. In such schemes, the sender and receiver rely on secret keys to establish a covert channel. However, conventional key exchange protocols would expose the key exchange process between them. An adversary who observes the key exchange would be aware of the existence of communication behavior. The keys used in covert communication are not suitable to be generated through conventional key exchange schemes. To address this, we propose a blockchain-based covert elliptic-curve Diffie-Hellman key exchange scheme (BCDH) to conceal the process of the key exchange in blockchain transactions. Following a straightforward setup, BCDH allows the sender and receiver to covertly exchange a secret key on a blockchain without direct communication. Furthermore, we expand the BCDH approach to operate across multiple blockchains, further enhancing its covertness and stability. We analyze BCDH from several perspectives, including covertness, security, randomness, etc. Additionally, we implement a prototype of BCDH on the Ethereum platform to assess its feasibility and performance. Our evaluation demonstrates that BCDH is efficient and well-suited for real-world applications.
Open access
Cryptography and Data Security
Advanced Steganography and Watermarking Techniques
The security proof of a protocol, though formally rigorous within a given model, is entirely contingent on the model's assumptions. If the adversary's capabilities are underspecified, the cryptographic primitives are idealized, or the security properties are incompletely formalized, the proof may not hold in practice.The first contribution advances prior work on refining symbolic models for crypto- graphic primitives to better capture their behaviors. Specifically, we propose more precise equational theories for the ElGamal cryptosystem, DSA signatures, and Zero-Knowledge Proofs. Standard symbolic modeling of these primitives disregards their algebraic prop- erties, which may lead to missed attacks in larger protocols. Additionally, we introduce a formal model of exponentiation and re-encryption Mix-Networks. By combining these models with our equational theories, we can automatically find attacks based on the incorrect use of the Mix-Networks missed by previous symbolic models.The second contribution involves analyzing the WireGuard protocol. We examine the protocol's claimed security properties under an adversary capable of compromising any possible key combinations. To systematize this analysis, we introduce the concepts of minimal defensive model and minimal offensive adversary model. The defensive models ensures that violating a security property requires possessing specific atomic capabilities. Minimal offensive models define the smallest sets of adversarial capabilities that break security. Theses derivations helped to identify an implementation optimization that introduces new attack vectors.The third contribution presents a hybrid protocol combining WireGuard and Post- Quantum WireGuard, aligning with recommendations for a secure transition to post- quantum cryptography. Although a symbolic analysis of PQ-WireGuard existed, we uncover discrepancies between the model and the protocol's specifications, including pre- viously missed Unknown Key-Share attacks. We propose fixes and ensure the hybrid protocol's security relies on both the corrected post-quantum and classical WireGuard protocols. We formally defined a hybrid protocol's security as when there exists both minimal defensive models dependent on post-quantum keys and defensive models depen- dent on classical keys. Our work underscores the importance of iterative analysis during design, as achieving hybrid security required repeated refinement between modeling and verification.
This paper presents a comprehensive examination of advanced cryptographic primitives and their instantiations, focusing on signature schemes, zero-knowledge proofs, and threshold encryption schemes. The foundational properties required for a secure and complete digital signature scheme are first outlined, with an emphasis on existential unforgeability. Zero-knowledge proofs are then examined in detail, including definitions of completeness, zero-knowledge, soundness, and simulation extractability, along with a discussion of universal versus non-universal proof systems. The core contribution lies in the design and analysis of a threshold encryption scheme based on Shamir’s secret sharing and a CLT encryption framework. The construction of a (t, P)-threshold encryption scheme is defined, introducing the concept of partial decryption simulability. The framework ensures that any (t + 1)-sized subset of parties can decrypt the ciphertext while maintaining security against adversarial attempts. Additionally, the implementation of universal zero-knowledge proof systems is discussed, highlighting the trade-offs between universal and specific SRS-based proofs. The instantiation of pseudorandom functions and their weak robustness properties is also examined, ensuring secure key management and resistance to adversarial key collisions. Through detailed analysis and construction, this work provides a solid foundation for building secure cryptographic systems with efficient threshold encryption and zero-knowledge proofs, contributing to the advancement of cryptographic protocols and their applications in secure communications and data protection.
An increasing number of DeFi protocols are gaining popularity, facilitating transactions among multiple anonymous users. State Manipulation is one of the notorious attacks in DeFi smart contracts, with price variable being the most commonly exploited state variable-attackers manipulate token prices to gain illicit profits. In this paper, we propose PriceSleuth, a novel method that leverages the Large Language Model (LLM) and static analysis to detect Price Manipulation (PM) attacks proactively. PriceSleuth firstly identifies core logic function related to price calculation in DeFi contracts. Then it guides LLM to locate the price calculation code statements. Secondly, PriceSleuth performs backward dependency analysis of price variables, instructing LLM in detecting potential price manipulation. Finally, PriceSleuth utilizes propagation analysis of price variables to assist LLM in detecting whether these variables are maliciously exploited. We presented preliminary experimental results to substantiate the effectiveness of PriceSleuth . And we outline future research directions for PriceSleuth.
We present a secure and efficient federated learning protocol for autonomous vehicles that resists data leaks, redundancy, and adversarial attacks. Our system combines fast zero-knowledge proofs and compressed Bloom filters to verify updates without exposing private data. Compared to traditional approaches, our method reduces proof sizes by 90 % (under 10 KB), memory by up to 75 %, and maintains accuracy with less than 4% degradation under 30% attack rates. The entire update cycle completes in under 600 ms, making it practical for real-time use in vehicles. This work advances trustworthy AI deployment in dynamic, resource-limited networks.
Wael Issa, Nour Moustafa, Benjamin Turnbull, Kim‐Kwang Raymond Choo
Sixth-generation (6G) wireless networks are set to transform the Internet of Things (IoT) by enabling faster, smarter, and more connected systems. These networks will bring together a wide range of devices, including cars, robots, industrial machines, and smartphones, to support edge intelligence and real-time decision-making. Federated learning (FL) supports this shift by allowing devices to collaboratively train models without sharing raw data, which helps to protect user privacy. Despite its advantages, FL faces significant security challenges, including poisoning attacks and Byzantine clients, both of which can compromise the training process and degrade the accuracy and reliability of the global model. Although existing methods can detect malicious updates, many advanced attacks still bypass statistical defenses relying on metrics such as median and distance. Thus, developing an FL system that ensures both reliable decision-making and privacy and security guarantees in IoT networks remains a significant challenge. This study introduces a Digital Twin-driven Blockchain-enabled Federated Learning (DT-BFL) framework designed for IoT networks. The framework creates a digital representation of the IoT environment to support secure and decentralized edge intelligence using blockchain and federated learning technologies. DT-BFL is built to detect and filter out potentially poisoned model updates from malicious participants. This is achieved through a new smart contract-enabled decentralized aggregation method called Local Updates Purify (LUP). LUP uses a two-stage filtering process: First, it applies Median Absolute Deviation (MAD) to initially remove outliers, then uses statistical features and clustering to separate honest from malicious updates before aggregating the global model. It also assigns a Trust Score (TS) to each participant based on how much their updates differ from the global model and then uses a genuine criterion to select honest clients by evaluating trust scores, update similarity, and deviation from the global model. Experimental results show that DT-BFL effectively defends against various poisoning attacks on datasets like MNIST, ToN-IoT, and CIFAR-10 using models such as CNN, MLP, ResNet, and DenseNet, and maintains high accuracy even when 50% of the clients are malicious. Using a permissioned blockchain further secures the system by enabling aggregation of the decentralized model and authentication of clients through smart contracts. The source code is available on https://github.com/UNSW-Canberra-2023/LUP .
Federated Learning is a transformative approach to building secure and resilient AI systems by enabling decentralized model training without exposing raw data. As part of Challenges and Solutions for Cybersecurity and Adversarial Machine Learning, this chapter examines its role in enhancing cybersecurity and mitigating adversarial threats, emphasizing its privacy-preserving capabilities and robustness against attacks. Key security challenges, including adversarial model poisoning, communication risks, and data privacy concerns, are analyzed alongside solutions such as differential privacy, secure aggregation, and robust optimization techniques. The discussion extends to Federated Learning's applications in critical sectors such as healthcare, finance, and edge computing, where secure AI deployment is essential. Addressing these challenges and proposing viable solutions, the chapter provides a comprehensive perspective on Federated Learning's potential to enhance AI security and resilience in adversarial environments.
Legacy centralized Electronic Document and Records Management Systems (EDRMS) are confronted with security vulnerabilities, single failure points, and regulatory compliance. This paper envisions a decentralized EDRMS with Secure Multi-Party Computation (SMPC) to ensure privacy-preserving data computation and a Federated Byzantine Agreement (FBA) for fault-tolerant consensus. Secure Multi-Party Computation provides secure computation with secret inputs without sacrificing individual data inputs, while the Federated Byzantine Agreement enhances system reliability by consensus despite faulty or malicious nodes. The proposed solution significantly increases data security, scalability, and compliance with regulatory requirements through cryptographic techniques and distributed protocols. The performance assessment demonstrates a significant rise in accuracy (93%), efficiency (92%), recall (94%), and F1-score (91%), performing better than traditional centralized models. The framework is a solid, privacy-compliant solution for businesses handling sensitive data, like healthcare and finance, to provide safe, resilient, and efficient document management in decentralized settings.
In this work, we present a concretely efficient lattice-based zero-knowledge proof system that satisfies straight-line extractability. At the heart of our construction is a novel, non-generic application of the Katsumata transform (CRYPTO 2021) to the widely adopted zero-knowledge framework of Lyubashevsky, Nguyen and Plançon (CRYPTO 2022). Our approach significantly optimizes the proof size, achieving a 2X reduction compared to prior generic techniques. This makes our scheme particularly well-suited for anonymous credential systems, secure multi-party computation, and proving quantum security, where straight-line extractability is essential.
The works of Garg et al. [S&P’24] (aka hinTS) and Das et al. [CCS’23] introduced the notion of silent threshold signatures (STS) - where a set of signers silently perform local computation to generate a public verification key. To sign a message, any set of t signers sign the message non-interactively and these are aggregated into a constant-sized signature. This paradigm avoids performing expensive Distributed Key Generation procedure for each set of signers while keeping the public verification key constant-sized.In this work, we propose the notion of committee-based silent threshold signature (c-STS) scheme. In a c-STS scheme, a set of signers initially perform a one-time setup to generate the verification key, and then a subset of signers are randomly chosen for an epoch to perform the threshold signing while the other signers are not authorized to sign during that epoch. This captures existing systems like Ethereum Altair and Dfinity where only a specific committee is authorized to sign in a designated epoch. The existing STS schemes cannot be extended to the committee setting because the signature verification only attests to the number of signing parties, not which committee they belong to.So, we upgrade hinTS to the committee setting by proposing Dyna-hinTS. It is the first c-STS scheme and it requires a onetime silent setup and generates a one-time public verification key that does not vary with the committee. Assuming a set of 1024 signers (with corrupt 682 signers), hinTS generates an aggregated signature in 1.7s whereas Dyna-hinTS generates it in 0.35s within a committee of 80 signers. This yields a 4.9× improvement over hinTS for signature generation at the cost of increasing signature verification time by 4% over hinTS. Dyna-hinTS supports general access structure, weighted signatures and improves existing multiverse threshold signatures.
As Internet technology develops swiftly, the significance of privacy protection is escalating in the realms of e-commerce, e-government and software security. Due to the combination of the benefits of proxy signatures and blind signatures, the proxy blind signature scheme not only distributes the workload across application networks but also effectively safeguards the confidentiality of sensitive information. Additionally, the identity-based proxy blind signature protocol can avoid the problem of heavy certificate management and is widely used in electronic commerce and other scenarios. However, some identity-based proxy blind signature protocols that rely on the large integer factorization problem and the discrete logarithm problem are unable to withstand from attacks from quantum computers. Furthermore, current lattice-based proxy blind signature protocols offer only heuristic security and require three rounds of information exchange during the signing phase. In this paper, we introduce a new two-round identity-based proxy blind signature scheme based on lattices. This scheme utilizes a zero-knowledge proof protocol on lattices as its core component to develop an interactive two-round signature scheme that is free from security proof vulnerabilities. Additionally, we validate the security of the proposed protocol within the random oracle model and conduct a performance analysis.