Blockchain Papers

Mohammed El Baraka, Siham Ezzouak

No abstract is available for this record.

Saara Saaninkoski

Permissionless blockchain operates as a fully decentralized, transparent, and immutable ledger. Preserving privacy in such systems is a complex challenge, as privacy cannot rely on restricting access or deleting data. Bitcoin, the first application of blockchain technology, was initially praised as an anonymous digital currency, but the transactions on the network have been shown to be relatively easy to trace. This realization has led to the development of advanced privacy-enhancing mechanisms with stronger anonymity guarantees. This thesis offers a comprehensive overview privacy-preserving techniques for permissionless blockchain through a systematic tertiary review of existing surveys. It identifies and categorizes key techniques such as zero-knowledge proofs, ring signatures, homomorphic encryption, secure multi-party computation and decentralized mixing protocols. Their capabilities to mitigate risks of linkability and information leakage, as well as limitations like computational overhead, are examined. Furthermore, unresolved challenges and research interests in the field are analyzed. By consolidating fragmented insights into a coherent and accessible resource, this work aims to support the privacy-aware development and adoption of blockchain applications. The findings highlight a fundamental trade-off between the privacy capabilities, efficiency, and trust assumptions of existing techniques. Privacy in permissionless blockchain often requires computationally complex cryptographic methods, leading to significant delays and increased costs for users. Efficiency can be improved by assuming some level of trust in entities or hardware, but this may conflict with the principle of decentralization. Although many powerful techniques exist, there is no universal solution, and the best results are achieved with combining techniques on a case-by-case basis. Current research on permissionless blockchain privacy focuses on improving efficiency, interoperability and usability of privacy preserving techniques. Additionally, regulatory compliance and accountability are critical concerns, as the technology must comply with privacy regulations while preventing anonymity in illegal activities such as money laundering.

Anna Kristyna

This work proposes an end-to-end architecture for secure data federation and privacy-preserving analytics across multi-tenant cloud environments using homomorphic encryption (HE). We address the core challenge of enabling cross-tenant joins, aggregations, and model scoring without exposing plaintext or weakening tenant isolation. The framework integrates schema-level federation with encrypted data lakes, columnar ciphertext packing for vectorized operations, and an adaptive HE planner that selects between CKKS for approximate analytics and BFV/BGV for exact computations. To bound latency while maintaining correctness, we apply batching, ciphertext relinearization, and rotation scheduling, and offload heavy primitives to accelerator-ready microservices. Policy-aware orchestration enforces per-tenant keys via cloud KMS and supports fine-grained access control and revocation. For sensitive workflows, we compose HE with complementary protections secure enclaves for control-plane logic, differential privacy on result releases, and zero-knowledge proofs to attest query policy compliance achieving defense-in-depth without collapsing the HE trust model. The system exposes SQL-like and DataFrame APIs, a query optimizer that estimates noise budgets and bootstrapping costs, and lineage-rich audit trails for regulatory reporting. We outline deployment patterns on containerized clusters, discuss cost/performance trade-offs under realistic workloads, and provide guidance on tenancy hardening (noisy neighbor resistance, side-channel hygiene). The result is a practical pathway for organizations to collaborate on analytics and machine learning across clouds and jurisdictions while preserving confidentiality, minimizing data movement, and meeting compliance obligations

Noah Foster

Federated Learning (FL) has emerged as a revolutionary machine learning approach, enabling the training of algorithms across decentralized devices or servers while maintaining data privacy. Unlike traditional centralized methods that pool data into a single repository, FL keeps data localized, enhancing the protection of sensitive information and ensuring compliance with privacy standards like GDPR and CCPA. This paradigm shift is particularly relevant in today's data-driven world, where concerns over data breaches and regulatory compliance are paramount. FL allows organizations and individuals to collaboratively train powerful machine learning models without sharing sensitive data. By adopting FL approaches, leveraging distributed data and computing power across different sources while respecting user privacy becomes possible. The architecture of FL involves a central system coordinating updates from multiple sources to improve a global model. Edge devices, such as smartphones or IoT devices, perform local training using their unique datasets. Each edge device trains the model locally, sending only updates (like gradients) to the central server, ensuring sensitive data is never exposed1. Furthermore, privacy-preserving technologies like differential privacy and homomorphic encryption strengthen data confidentiality and compliance with regulations. Differential privacy introduces noise to data or model updates to prevent the reconstruction of individual information, while homomorphic encryption allows computations on encrypted data without decryption1. The rise of 5G networks will significantly enhance FL by reducing latency and improving communication between edge devices and central servers, enabling faster model training and real-time applications. Blockchain technology offers a decentralized and immutable ledger for tracking data usage and model updates, creating a transparent and tamper-proof mechanism, addressing trust issues in federated systems and further strengthening security

Kiayias, Aggelos, Koutsoupias, Elias, Lazos, Philip, Panagiotakos, Giorgos

Handling congestion in blockchain systems is a fundamental problem given that the security and decentralization objectives of such systems lead to designs that compromise on (horizontal) scalability (what sometimes is referred to as the "blockchain trilemma"). Motivated by this, we focus on the question whether it is possible to design a transaction inclusion policy for block producers that facilitates fee and delay predictability while being incentive compatible at the same time. Reconciling these three properties is seemingly paradoxical given that the dominant approach to transaction processing is based on first-price auctions (e.g., as in Bitcoin) or dynamic adjustment of the minimum admissible fee (e.g. as in Ethereum EIP-1559) something that breaks fee predictability. At the same time, in fixed fee mechanisms (e.g., as in Cardano), fees are trivially predictable but are subject to relatively inexpensive bribing or denial of service attacks where transactions may be delayed indefinitely by a well funded attacker, hence breaking delay predictability. In this work, we set out to address this problem by putting forward blockchain space tokenization (BST), namely a new capability of a blockchain system to tokenize its capacity for transactions and allocate it to interested users who are willing to pay ahead of time for the ability to post transactions regularly for a period of time. We analyze our system in the face of worst-case transaction-processing attacks by introducing a security game played between the mempool mechanism and an adversary. Leveraging this framework, we prove that BST offers predictable and asymptotically optimal delays, predictable fees, and is incentive compatible, thus answering the question posed in the affirmative.

Zenghua Du, Jingxian Hu, Sheng Yang, Xuejian Qin · 6 authors

No abstract is available for this record.

Kiran Kumar Kondru, R. Saranya

Blockchains like Hyperledger Fabric are comparably faster than public blockchains like Ethereum. These Permissioned Blockchains do not need to consider for in-built security like Bitcoin. The core part of Hyperledger Fabric, the orderer, replicates blocks across the blockchain network. The orderer f

Nicholas Brandt, Dennis Hofheinz, Michael Klooß, Michael Reichle

We construct the first blind signature scheme that achieves all of the following properties simultaneously: – it is tightly secure under a standard (i.e., non-interactive, non-q-type) computational assumption, – it does not require pairings, – it does not rely on generic, non-black-box techniques (like generic NIZK proofs). The third property enables a reasonably efficient solution, and in fact signatures in our scheme comprise 10 group elements and 29 Zp-elements. Our scheme starts from a pairing-based non-blind signature scheme (Abe et al., JoC 2023), and uses recent techniques of Chairattana-Apirom, Tessaro, and Zhu (CRYPTO 2024) to replace the pairings used in this scheme with non-interactive zero-knowledge proofs in the random oracle model. This conversion is not generic or straightforward (also because the mentioned previous works have converted only significantly simpler signature schemes), and we are required to improve upon and innovate existing techniques in several places. As an interesting side note, and unlike previous works, our techniques only require a non-programmable random oracle, and our signature scheme achieves predicate blindness (which means that the user can prove state ments about the signed message during the signing process).

Nday Kabulo Sinai, Hoh Peter In

Public blockchains, such as Ethereum, rely on decentralized networks of peer-to-peer nodes known as validators or miners to verify all transactions and create new valid blocks. These validators can prioritize transactions, primarily based on high gas fees, allowing miners to maximize their block rewards, a concept referred to as maximal extractable value (MEV). However, MEV is vulnerable to front-running, back-running, and sandwich attacks (FBSAs), and is exploited by malicious nodes and bots to manipulate users’ valuable transactions. These malicious activities adversely impact the Blockchain’s scalability, transparency, and security. Flashbots, as one of the solutions, introduces centralization since all nodes have to forward all blocks to the central node. To address these issues, we have designed a new Blockchain transaction ordering protocol called Quantum Random Transaction Ordering Protocol (Q-RTOP). The proposed protocol operates on top of the existing Blockchain transaction ordering mechanism. However, instead of allowing validators to select transactions based on high gas fees, decentralized nodes running Q-RTOP securely randomize all transactions and then forward them to the validators, which proceed with the block validation without any change. Our protocol primarily focuses on randomizing transactions before being processed by the validators by utilizing a quantum random generator as a secure source of randomness. The final results demonstrated that Q-RTOP effectively secured user transactions and randomized 8192 transactions within 25 milliseconds.

Seyi Oladimeji, Peter Broklyn, Axel Egon

No abstract is available for this record.

Adnan Kılıç, Cansu Betin Onur, Ertan Onur

We present a password‐authenticated (2, 3)‐threshold group key share (PATS) mechanism. Although PATS resembles threshold secret sharing schemes, it has a different structure. The innovative perspective of the PATS mechanism that makes a difference from the standard secret‐sharing schemes is that it involves parties in the generation of the shares. PATS allows parties to communicate securely to establish their shares over insecure channels. Parties (shareholders) construct a secret (key) using shares obtained at the end of the protocol. PATS takes advantage of zero‐knowledge proofs compared to well‐known threshold key exchange schemes and will tolerate the existence of semi‐trusted parties. We present two variants of PATS, centralized and distributed, and then generalize PATS to ( t , n )‐threshold scheme. PATS supports the distributed operation and optionally facilitates group key verification by a trusted third party, which may also partake in group key sharing. In this paper, we present PATS, which employs finite fields and elliptic curves, along with its security and complexity analyses.

Jialiang Fan, Qianhong Wu, Qin Wang, Kun Wang · 5 authors

No abstract is available for this record.

Shankar Prasad Sapkota

No abstract is available for this record.

Ahmed I. Alutaibi

Every day, a vast amount of data with incalculable value will be generated by the IoT devices that are deployed in various types of applications. It is crucial to ensure the reliability and safety of IoT data exchange in a cloud context because this data frequently contains the user's private information. This study presents a novel encrypted data storage and security system using the blockchain method in conjunction with hybrid optimization-based proxy re-encryption (HO-PREB). Dependency on outside central service providers is eliminated by the HO-PREB-based consensus process. In the blockchain system, several consensus nodes serve as proxy service nodes to restore encrypted data and merge transformed ciphertext with private data. Hybrid owl and bat optimization is employed to select the optimal key for enhancing security. This removes the limitations associated with securely storing and distributing private encrypted data via a distributed network. Moreover, the blockchain's distributed ledger ensures the permanent storage of data-sharing records and outcomes, ensuring accuracy and dependability. The simulated experiments of the designed model are evaluated with existing cryptographic techniques and gain a lower latency of 3.2 s and a lower turnaround time of 45 ms. Furthermore, the developed technique enhances cloud system security and possesses the capability to detect and mitigate attacks in the cloud environment.

Dahong Qian, Yiyang Xu, Yuncong Hu

No abstract is available for this record.

Mahmudun Nabi, Sepideh Avizheh, Preston Haffey, Reihaneh Safavi–Naini · 5 authors

With the rise of decentralized systems and applications that run over multiple blockchains, there is a growing need for architectures and bridges that ensure the trusted transfer of data and assets between the chains. zkBridge (ACM CCS'22) is a cross-chain bridge protocol that was proposed for non-permissioned blockchain and uses a network of relays, each working (for example as a light node) on a blockchain. A relay communicates with a corresponding smart contract on another chain to transfer data from the first chain to the second, and the smart contract on the second chain is used to verify the correctness of the received data from the first chain. zkBridge designs and implements optimized zero-knowledge proofs, that minimize the work of the smart contract on the second chain to verify the correctness of the received data. In this paper, we consider applications that work across two or more permissioned blockchains. We propose sigBridge that uses the framework of zkBridge but replaces the costly zero-knowledge proof computation with a pair of algorithms based on the consensus algorithm of the first chain. The algorithms will be run by a relay node on the first chain and a smart contract on the second chain and provide verifiability of data and asset transfer from the first chain to the second. The pair of algorithms are significantly more efficient compared to generating zero-knowledge proofs, verifying them, or running the full consensus algorithm of the first blockchain. We then show how a decentralized user-centric resource-sharing application will work over this architecture. We give a proof-of-concept implementation of an attribute-based access control system for a resource-sharing application that runs over two private Ethereum blockchains, and report the computation costs of the protocol.

Yuming Tang, Yitian Zhang, Tao Niu, Zhen Li · 7 authors

Federated Learning (FL), as an emergent paradigm in privacy-preserving machine learning, has garnered significant interest from scholars and engineers across both academic and industrial spheres. Despite its innovative approach to model training across distributed networks, FL has its vulnerabilities; the centralized server-client architecture introduces risks of single-point failures. Moreover, the integrity of the global model—a cornerstone of FL—is susceptible to compromise through poisoning attacks by malicious actors. Such attacks and the potential for privacy leakage via inference starkly undermine FL’s foundational privacy and security goals. For these reasons, some participants unwilling use their private data to train a model, which is a bottleneck in the development and industrialization of federated learning. Blockchain technology, characterized by its decentralized ledger system, offers a compelling solution to these issues. It inherently prevents single-point failures and, through its incentive mechanisms, motivates participants to contribute computing power. Thus, blockchain-based FL (BCFL) emerges as a natural progression to address FL’s challenges. This study begins with concise introductions to federated learning and blockchain technologies, followed by a formal analysis of the specific problems that FL encounters. It discusses the challenges of combining the two technologies and presents an overview of the latest cryptographic solutions that prevent privacy leakage during communication and incentives in BCFL. In addition, this research examines the use of BCFL in various fields, such as the Internet of Things and the Internet of Vehicles. Finally, it assesses the effectiveness of these solutions.

Jisheng Dong, Qingni Shen, Junkai Liang, Cong Li · 6 authors

No abstract is available for this record.

Ana Petrovska, Gabriele Gelardi, Hüseyin Demirci, Emre Koçyiğit · 10 authors

No abstract is available for this record.

Lin Jiao, Yongqiang Li, Yonglin Hao, Xinxin Gong

As the practical applications of fully homomorphic encryption (FHE), secure multi‐party computation (MPC) and zero‐knowledge (ZK) proof continue to increase, so does the need to design and analyze new symmetric‐key primitives that can adapt to these privacy‐preserving protocols. These designs typically have low multiplicative complexity and depth with the parameter domain adapted to their application protocols, aiming to minimize the cost associated with the number of nonlinear operations or the multiplicative depth of their representation as circuits. In this paper, we propose two differential fault attacks against a one‐way function RAIN used for Rainier (CCS 2022), a signature scheme based on the MPC‐in‐the‐head approach and an FHE‐friendly cipher HERA used for the RtF framework (Eurocrypt 2022), respectively. We show that our attacks can recover the keys for both ciphers by only injecting a fault into the internal state and requiring only one normal and one faulty ciphertext blocks. Thus, we can use only the practical complexity of 2 26.6 /2 28.8 /2 30.4 bit operations to break the full‐round RAIN with 128/192/256‐bit keys. For full‐round HERA with 80/128‐bit key, our attack is practical with complexity the complexity of 2 20 encryptions with about 2 16 memory.

Moumita Dutta, Chaya Ganesh, Neha Jawalkar

No abstract is available for this record.

He Qin, Xiaofeng Ma, Dawei Zhang, Feng Peng

Decentralized identity represents an innovative approach based on blockchain to achieve effective identity management. This method utilizes decentralized identifiers and verifiable credentials to enable trusted authentication, free circulation of identity information, and self-sovereign control over identity data functionalities. The current decentralized identity systems rely on entirely anonymous identifiers, lacking robust identity regulation. Furthermore, they face challenges such as identity attribute leakage during verifiable credential presentation and the issuers’ struggle to reliably revoke credentials. To address these issues, efficient and practical schemes have been designed based on BBS signature, zero-knowledge proof, dynamic accumulator, and blockchain technology: one for decentralized identifiers management and the other for verifiable credential privacy protection, both of which are supervised and revocable. The former ensures the privacy of subject identity while achieving regulatability and revocability of identity data by the regulator. The latter facilitates selective disclosure of anonymous credentials and reliable revocation. A security analysis shows that the proposed scheme meets anonymity, non-forgeability, regulatory reliability, and revocability reliability, and offers comprehensive and effective privacy protection measures. The experimental results demonstrate that the algorithms designed operate at a millisecond level, which satisfies the demands of blockchain identity management scenarios.

Nidhish Bhimrajka, Ashish Choudhury, Supreeth Varadarajan

No abstract is available for this record.

Jing Wang, Xue Yuan, Yingjie Xu, Yudi Zhang

Large language models (LLMs) have brought significant advancements to artificial intelligence, particularly in understanding and generating human language. However, concerns over management burden and data security have grown alongside their capabilities. To solve the problem, we design a blockchain‐based distributed LLM framework, where LLM works in the distributed mode and its outputs can be stored and verified on a blockchain to ensure integrity, transparency, and traceability. In addition, a multiparty signature‐based authentication mechanism is necessary to ensure stakeholder consensus before publication. To address these requirements, we propose a threshold elliptic curve digital signature algorithm that counters malicious adversaries in environments with three or more participants. Our approach relies on discrete logarithmic zero‐knowledge proofs and Feldman verifiable secret sharing, reducing complexity by forgoing multiplication triple protocols. When compared with some related schemes, this optimization speeds up both the key generation and signing phases with constant rounds while maintaining security against malicious adversaries.