In many fields, the need to securely collect and aggregate data from distributed systems is growing. However, designs that rely solely on encrypted data transmission make it difficult to trace malicious users. To address this challenge, we have enhanced the secure aggregation (SA) protocol proposed by Bell et al. (CCS 2020) by introducing verification features that ensure compliance with user inputs and encryption processes while preserving data privacy. We present LZKSA, a quantum-safe secure aggregation system with input verification. LZKSA employs seven zero-knowledge proof (ZKP) protocols based on the Ring Learning with Errors problem, specifically designed for secure aggregation. These protocols verify whether users have correctly used SA keys and their L∞, L2 norms and cosine similarity of data, meet specified constraints, to exclude malicious users from current and future aggregation processes. The specialized ZKPs we propose significantly enhance proof efficiency. In practical federated learning scenarios, our experimental evaluations demonstrate that the proof generation time for L∞ and L2 constraints is reduced to about 10-3 of that required by the current state-of-the-art method, RoFL (S&P 2023), and ACORN (USENIX 2023). For example, the proof generation/verification time of RoFL, ACORN and LZKSA for L∞ is 94s/29.9s, 78.7s/33.9s, and 0.02s/0.0062s for CIFAR10, respectively.
Daria Schumm, Gabriel Stegmaier, Cedric von Rauscher, Katharina Müller · 5 authors
Blockchains raise new privacy challenges, especially in Decentralized Identity (DI) and Self-Sovereign Identity (SSI) systems. Zero Knowledge Proofs (ZKPs) offer privacy, but only allow binary verification. Homomorphic Encryption (HE) enables flexible operations on encrypted data (e.g., addition, multiplication) but lacks comparison support. This paper addresses this gap by introducing a privacy-preserving comparison operation within HE, presenting the first comprehensive comparison of ZKP and HE as privacy-preserving mechanisms.
Pierre Ghaly, Harald Gjermundrød, Ioanna Dionysiou
Blockchain tokenization ecosystems face significant challenges in complying with privacy regulations such as the General Data Protection Regulation (GDPR), particularly the “Right to Be Forgotten” mandate. The immutable nature of blockchain conflicts with the requirement for data deletion, creating a fundamental tension between technological capabilities and regulatory compliance. This paper presents a novel cryptographic audit framework for implementing GDPR-compliant data erasure in configurable tokenization systems. Our approach leverages cryptographic key destruction, zero-knowledge proofs for audit trails, and automated smart contract mechanisms to achieve practical data deletion while preserving blockchain immutability. The framework introduces a triple-layer architecture separating on-chain and off-chain references from off-chain sensitive data, enabling verifiable data erasure through cryptographic “shredding” techniques. We demonstrate the framework’s effectiveness through detailed algorithms and present a proof of concept comprehensive audit mechanism that generates cryptographic proofs of successful data deletion without revealing sensitive information. Our solution addresses critical gaps in current blockchain privacy implementations and provides a practical pathway for regulatory compliance in tokenization ecosystems.
Blockchain Technology Applications and Security
Cryptography and Data Security
Physical Unclonable Functions (PUFs) and Hardware Security
Blockchains are considered for healthcare data sharing due to their immutability, decentralization, and auditability. However, ledger transparency exposes on-chain identifiers and activity metadata, enabling linkage across pseudonyms and inference over user behavior. Prior work has primarily focused on content confidentiality and access control, while leaving identity unlinkability insufficiently addressed. To this end, we present an approach that integrates Account ion (AA), zeroknowledge proofs (Groth16), and Pedersen commitments. The approach embeds proof- and commitment-based verification into programmable smart contract accounts (SCAs), enabling authentication without disclosing identifiers and decoupling transactions from static keys. We develop a proof-of-concept on the Polygon Amoy testnet using Circom and Solidity, and evaluate privacy under a global, passive, external, static, and computationally bounded attacker. For the ERC-4337 comparison, the attacker is assumed to know user-SCA mappings; for the account-shuffling comparison, the attacker knows one SCA per user. Using entropy metrics and clustering-based inference over on-chain metadata, our approach achieves the maximum entropy of $\log _{2}(10) \approx 3.32$ in a ten-user setting (versus 0 for ERC-4337 as specified, i.e., without privacy extensions) and substantially reduces clustering accuracy relative to address shuffling (ARI $0.468 \rightarrow 0.038$, NMI $0.653 \rightarrow 0.177$), while maintaining the auditability required for healthcare governance.
The inherent complexity of cryptographic addresses in Web3 ecosystems poses significant usability problems. Decentralized Domain Services attempt to solve this challenge yet, in the meantime, jeopardize users’ pseudo-anonymity. This paper investigates the design and implementation of a decentralized naming system leveraging IOTA’s Move smart contract framework. We analyze the system architecture and provide a comparative evaluation against established domain name services such as ENS, SuiNS, SolanaNS, and Avvy, highlighting the unique advantages and limitations of our proposed solution. Moreover, we introduce a novel committee-bridge architecture managing off-chain hashed name storage, enabling selective disclosure of domain information while maintaining low computational overhead. We also present results from an evaluation that assesses the functioning and the performance of the system, which confirm the viability of the proposal.
Time-sensitive Internet of Things (IoT) deployments need fine-grained, auditable authorisation without exposing payloads to intermediaries or embedding access policy in cipher-text. The Secure IoT Communication and Policy Enforcement (SCOPE) framework separates on-ledger authorisation from end-to-end content protection while keeping intermediaries minimally trusted. The SCOPE framework comprises a Broker Smart Contract (BSC) that records authorisation decisions, a decentralised Trusted Authority (TA) that issues committee attestations and epoch-scoped revocation snapshots, and a stateless edge relay that verifies requests and forwards ciphertext without decryption. Payload confidentiality and integrity are provided end-to-end by a pairing-free authenticated encryption with associated data (AEAD) channel with ephemeral key agreement and disciplined nonces, yielding replay resistance and forward secrecy with respect to the sender’s key. A prototype on a permissioned distributed ledger runtime, evaluated on an IoT edge testbed, demonstrates sub-second end-to-end operation, on-ledger authorisation within 500 ms, and lower computational latency than pairing-based Ciphertext-Policy Attribute-Based Encryption and Attribute-Based Signcryption (CP-ABE/ABSC) baselines, including BLUMA (multi-authority CP-ABE with hidden policy). The design is portable across ledgers and supports a drop-in post-quantum key-encapsulation mechanism plus AEAD (KEM+AEAD) channel without changes to the policy or relay planes, enabling auditable authorisation for multi-stakeholder settings such as smart ports, industrial automation, and e-health.
Hongzhi Li, Dun Li, Gang Lv, Noël Crespi · 8 authors
The widespread application of medical information systems has promoted the growth of personal electronic medical records (EMRs), which are typically produced in different medical institutions and stored in data centers. Consequently, data owners no longer retain control over their medical data, nor can they establish access control rules for their EMRs. Therefore, this study designs a patient-centered EMR access control system that integrates decentralized smart contracts and role-based access control (RBAC) to provide fine-grained data access control. In this system, we integrate a role-based access control model to achieve user-permission definition and adopt a personalized data access policy definition mechanism to achieve patient-centered data access control. The proposed system allows data owners to define a series of data access policies through smart contracts, achieving decentralized management of data access control permissions. In addition, we analyze the security features of this scheme and design a series of comparative experiments to evaluate the performance. The experimental results show that this system can efficiently achieve access control of personal electronic medical records and has higher reliability compared to traditional cloud-based EMR sharing systems.
Blockchain technology has emerged as a revolutionary paradigm for secure, transparent, and tamper-resistant data management. It offers a decentralized ledger where transactions are validated and recorded across a distributed network of nodes, eliminating the need for centralized authorities. Despite its widespread adoption across diverse domains—such as finance, supply chain, healthcare, and digital identity—blockchain still faces significant challenges in ensuring complete security and privacy. This paper addresses these challenges by proposing a novel security and privacy algorithm designed specifically to enhance blockchain resilience against evolving threats. The proposed approach integrates hybrid cryptography, pseudonymous identifiers, and an optimized consensus mechanism to achieve a balanced trade-off between security, privacy, and computational efficiency. The hybrid cryptographic model combines symmetric and asymmetric encryption techniques to safeguard transaction data at multiple layers. Symmetric encryption ensures fast and secure data exchange, while asymmetric keys are used for identity verification and secure key distribution. To further strengthen user anonymity, the algorithm incorporates pseudonymous identity management, which replaces permanent public keys with dynamically generated pseudonyms. These pseudonyms are refreshed periodically to prevent link ability between consecutive transactions, ensuring that individual identities remain hidden even if certain nodes or data patterns are compromised. Additionally, the optimized consensus protocol enhances transaction validation efficiency by reducing redundant computations and improving synchronization among nodes. This approach minimizes latency and energy consumption while maintaining strong resistance against consensus-based attacks such as 51% or Sybil attacks. Extensive simulations and experimental evaluations were conducted to measure the algorithm’s performance under various network conditions and adversarial scenarios. The results demonstrate that the proposed model significantly improves transaction validation speed and reduces cryptographic overhead compared to traditional Proof-of-Work and Proof-of-Stake systems.
Blockchain has emerged as a robust foundation for decentralized trust, secure data sharing, and immutable record keeping. However, its inherently transparent architecture creates significant privacy challenges when applied in sensitive domains such as healthcare, finance, identity management, and IoT. Although privacy-preserving techniques including Zero-Knowledge Proofs (ZKPs), Attribute-Based Encryption (ABE), homomorphic encryption, ring signatures, mixers, and hybrid off-chain storage mechanisms have demonstrated partial effectiveness, they remain limited by high computational overhead, poor scalability, interoperability constraints, and regulatory complications. These challenges hinder the practical deployment of blockchain in real-world, data-intensive environments. This review examines key blockchain privacy issues and synthesizes major research contributions from contemporary literature. It further emphasizes the importance of hybrid privacy-preserving models to balance transparency, confidentiality, and storage efficiency. The analysis reinforces the relevance of solutions such as ChainGuard, a dual-chain architecture that maintains sensitive data on a private blockchain while using a public chain to store verifiable hash references. This approach directly mitigates the transparency–privacy conflict, storage inefficiencies, and cryptographic performance limitations identified across existing studies. The paper concludes by outlining research gaps and proposing future directions for scalable, interoperable, and regulation-aligned blockchain privacy systems.
Zero-knowledge proof (ZKP) circuits implemented in programming languages like Circom are fundamental to blockchain and privacy-preserving applications. These code often suffer from constraint-related issues where constraints fail to accurately specify intended computations. While existing analysis tools have been proposed, they struggle with large-scale circuits containing complex template embeddings. We present ScaleCirc, a novel framework that addresses such limitations through: 1) systematic management of analysis redundancy via circuit deduplication strategies; 2) constrainedness propagation methods leveraging source code semantic information; and 3) a generalizable framework for different circuit analysis tasks. Evaluation on 691 real-world circuits shows ScaleCirc demonstrates higher efficiency, and successfully analyzes many Circom programs that existing works failed on.
Physical Unclonable Functions (PUFs) and Hardware Security
The NFT ecosystem represents an interconnected, decentralized environment that encompasses the creation, distribution, and trading of Non-Fungible Tokens (NFTs), where key actors, such as marketplaces, sellers, and buyers, utilize smart contracts to facilitate secure, transparent, and trustless transactions. Scam tokens are deliberately created to mislead users and facilitate financial exploitation, posing significant risks in the NFT ecosystem. Prior work has explored the NFT ecosystem from various perspectives, including security challenges, actor behaviors, and risks from scams and wash trading, leaving a gap in understanding the semantics and interactions of smart contracts during transactions, and how the risks associated with scam tokens manifest in relation to the semantics and interactions of contracts. To bridge this gap, we conducted a large-scale empirical study on smart contract semantics and interactions in the NFT ecosystem, using a curated dataset of nearly 100 million transactions across 20 million blocks on Ethereum. We observe a limited semantic diversity among smart contracts in the NFT ecosystem, dominated by proxy, token, and DeFi contracts. Marketplace and proxy registry contracts are the most frequently involved in smart contract interactions during transactions, engaging with a broad spectrum of contracts in the ecosystem. Token contracts exhibit bytecode-level diversity, whereas scam tokens exhibit bytecode convergence. Certain interaction patterns between smart contracts are common to both risky and non-risky transactions, while others are predominantly associated with risky transactions. Based on our findings, we provide recommendations to mitigate risks in the blockchain ecosystem, and outline future research directions.
Trading of data is increasingly prevalent as data gain significant economic value, but existing data exchange schemes often suffer from third-party dependency, high verification costs, or inadequate protection of fairness and confidentiality. An efficient decentralized fair exchange scheme for data trading which uses cryptographic commitment scheme and smart contract was proposed in this paper. Our solution guarantees exchange fairness, which requires payments and data to be exchanged correctly between the data buyer and the data seller. First, we design a data verification method with constant verification cost by using polynomial commitments, ensuring that the buyer receives the data matching an agreed-upon commitment. Second, we employ smart contracts to complete the atomic exchange of data and funds, and design a key transmission method by using the properties of bilinear pairings to ensure the confidentiality of trading data. Moreover, our scheme was proved to satisfy the desired security properties: seller fairness, buyer fairness and confidentiality. Simulation results demonstrate the efficiency and practicality of the proposed scheme.
Cross-chain payment, serving as critical infrastructure for multi-chain ecosystem interoperability, confronts the fundamental challenge of simultaneously ensuring privacy preservation, regulatory compliance, and quantum-resistant security—objectives that are inherently difficult to reconcile. This paper proposes a Lattice-based Dynamic Privacy-preserving Cross-chain Payment Scheme (LDPCPS) that innovatively integrates advanced cryptographic primitives. Specifically, LDPCPS employs a privacy-preserving scalar product (PPSP) protocol enabling ciphertext-domain aggregation and verification, constructs a dynamic regulatory framework using signatures of knowledge (SoK) for zero-knowledge compliance proofs and risk-triggered traceability, and implements proxy re-encryption to facilitate seamless quantum-resistant key migration. Experimental results demonstrate that LDPCPS has significant superiority over state-of-the-art alternatives in quantum resistance, computational efficiency, and regulatory adaptability, thereby establishing a robust foundation for secure and compliant cross-chain transactions.
Cryptography and Data Security
Physical Unclonable Functions (PUFs) and Hardware Security
As the blockchain ecosystem continues to diversify, the lack of interoperability among heterogeneous blockchain systems has become a critical bottleneck, leading to fragmented data silos and limited collaboration. Although numerous cross-chain protocols—such as atomic swaps, sidechains, and relay-based mechanisms—have been introduced to address this issue, they often face significant challenges related to privacy, security, and decentralization. In this paper, we propose a novel cross-chain protocol that enhances traditional hash-locking mechanisms by integrating zero-knowledge proofs and chameleon hash functions. Our approach ensures strong path confidentiality, such that reconstructing the payment path is computationally infeasible under the discrete logarithm assumption, even in partially compromised networks. Additionally, we introduce a multi-path atomic swap framework that supports concurrent routing and preserves transactional autonomy, enabling users to flexibly select preferred payment paths. We evaluate the performance through theoretical analysis and simulation. Comparative results demonstrate that our solution achieves secure atomicity with minimal trust assumptions and improved latency compared to existing methods.
Hang Liu, Ming Yang, Aotian Cai, Chenhao Wang · 5 authors
In recent years, with the increasing prevalence of online group chat applications, malicious information has been more easily disseminated on the internet. Asymmetric group message franking (AGMF) allows users to report received malicious messages to moderators, achieving content moderation in large-scale online end-to-end messaging systems. However, the state-of-the-art construction is built upon traditional public key cryptosystems, resulting in the complex certificate management problem. This paper systematically explores identity-based AGMF (IB-AGMF) to resolve this issue. Specifically, we first introduce a novel primitive called hash proof system-based anonymous identity-based key encapsulation mechanism supporting sigma protocol (HPS-AIB-KEMΣ) and present a practical construction based on DBDH assumption. After formalizing the concept and security notions of IB-AGMF, we propose the generic construction of IB-AGMF based on HPS-AIB-KEMΣand non-interactive zero knowledge proof system. Finally, we conduct comprehensive performance evaluations and comparisons to demonstrate the feasibility of IB-AGMF in group communication scenarios.
Xin Liu, Anyang Qi, Lanying Liang, Dan Luo · 10 authors
In computer vision, the intersection determination of polygonal areas is utilized to segment different regions in an image and assist in detecting the boundaries of the regions. Moreover, the secure computation of the intersection area of polygons can solve the private calculation of geometric problems in machine learning. A security protocol under the semi-honest model was designed for the problem of secure computation of the intersection area of two polygons. This protocol adopts a new coding method and the Paillier homomorphic encryption algorithm. Aiming at the malicious behaviors that malicious participants may carry out in the semi-honest protocol, a secure computation protocol for the intersection area of polygons under the malicious model was designed by using methods such as hash function, cut-and-choose and zero-knowledge proof. The security of this protocol was proved, and its computational complexity and communication complexity were analyzed. Compared with the existing schemes, it is more efficient.
This paper proposes DAPUR, a decentralized anonymous payment protocol that simultaneously achieves strong transaction privacy and regulatory compliance. Addressing the fundamental tension between confidentiality and oversight in cryptocurrency systems, we develop a novel cryptographic framework combining zero-knowledge proof with access-controlled encryption. The protocol enables end-to-end transaction privacy while permitting authorized regulators to audit transaction details through selective disclosure mechanisms. The system’s security is formally proven through a model establishing ledger indistinguishability, transaction non-malleability, and balance preservation. Experimental results demonstrate practical viability with sub-second verification times. DAPUR represents a significant advance in privacy-preserving payment systems, showing that regulatory compatibility can be achieved without compromising decentralization principles.
Sai Srinivas Vellela, Lakshma Reddy Vuyyuru, Sudhir Kumar Jidugu, M. Purnachandra Rao · 6 authors
The concept of blockchain technology has transformed the digital ecosystem to allow decentralized, transparent and immutable transactions in various sectors. Its security is closely dependent on classical cryptography like ECDSA and RSA to perform digital signatures and SHA-256 to achieve consensus, which are becoming more susceptible to quantum computing. The cryptographic principles underpinning blockchain could be compromised with the emergence of the quantum algorithms of Shor and Grover, endangering the integrity of transactions, authentication and consensus protocols. This paper discusses the implications that quantum computing could have on blockchain security, analyses vulnerabilities of current cryptographic primitives and assesses post-quantum cryptographic (PQC) protocols, including lattice-based protocols, hash-based protocols, and code-based protocols. To guarantee backwards compatibility, as well as a gradual upgrade process to quantum-resistant protocols, a hybrid migration approach that involves transactions with two signatures is suggested. Experimental analysis shows that PQC integration attains reasonable performance trade-offs, that preserve verification costs, block size growth, and throughput effects within feasible limits, and that zero-loss resilience is achieved during reorganization tests. The conclusion is that blockchains with PQC support can reach post-quantum levels of security without major operation interruption, and they provide a viable roadmap to moving towards distributed ledger systems that are future-ready.