Blockchain Papers

Diego F. Aranha, Anamaria Costache, Antonio Guimarães, Eduardo Soria-Vázquez

No abstract is available for this record.

Fuchun Joseph Lin, Chaoping Xing, Yizhou Yao

No abstract is available for this record.

Yu‐Wen Cheng, Yijun Jing, Zicheng Shi, Chuan-Kun Wu

In recent years, protecting data privacy in distributed environments has become a key challenge with the rise of the Internet of Things (IoT) and edge computing. Federated learning helps mitigate the risk of data leakage by keeping data local, but issues such as authentication and insufficient privacy protection remain. In this paper, we propose a cloud-edge-end federated learning privacy protection scheme (FL-DPASS) that incorporates differential privacy and secret sharing. We introduce a Schnorr zero-knowledge proof authentication mechanism to secure end devices' access without revealing their identities. Additionally, we combine differential privacy and secret sharing to enhance security during model parameter transmission and aggregation. Experimental results demonstrate that our scheme strengthens data privacy protection while maintaining training efficiency and accuracy.

Binay Kumar Gupta, Ananya Gupta, Junaid Alam, Soumyadev Maity

This paper presents a blockchain-based distributed fellowship management system incorporating Attribute-Based Encryption (ABE) and Zero-Knowledge Proofs (ZKP) to verify candidate eligibility without exposing sensitive information. The system ensures that only qualified candidates who meet predefined criteria can receive fellowships, reducing the likelihood of fraudulent applications and administrative errors. Blockchain technology creates an immutable, decentralized ledger that provides transparency and accountability, while ABE ensures fine-grained access control over sensitive data. The integration of ZKP enhances privacy by allowing candidates to prove their eligibility without revealing personal details. This approach streamlines the fellowship verification process, lowers administrative burdens, and increases the dependability and fairness of the system. Our proposal delivers a secure, privacy-preserving solution for managing fellowships with improved efficiency and trust.

Marco Gomes, Luís Costa, Patrícia Anjos Azevedo

No abstract is available for this record.

Gholamreza Ramezan, Eladio Robles Casas, Ben Beath, Jake Godfrey

No abstract is available for this record.

Dung Bui, Haoyue Chu, Geoffroy Couteau, Xiao Wang · 7 authors

Abstract We propose a generic compiler that can convert any zero-knowledge (ZK) proof for SIMD circuits to general circuits efficiently, and an extension that can preserve the space complexity of the proof systems. Our compiler can immediately produce new results improving upon state of the art. By plugging in our compiler to Antman, an interactive sublinear-communication protocol, we improve the overall communication complexity for general circuits from $$\mathcal {O}(C^{3/4})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mo>(</mml:mo> <mml:msup> <mml:mi>C</mml:mi> <mml:mrow> <mml:mn>3</mml:mn> <mml:mo>/</mml:mo> <mml:mn>4</mml:mn> </mml:mrow> </mml:msup> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> to $$\mathcal {O}(C^{1/2})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mo>(</mml:mo> <mml:msup> <mml:mi>C</mml:mi> <mml:mrow> <mml:mn>1</mml:mn> <mml:mo>/</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> . Our implementation shows that for a circuit of size $$2^{27}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mn>2</mml:mn> <mml:mn>27</mml:mn> </mml:msup> </mml:math> , it achieves up to $$83.6\times $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>83.6</mml:mn> <mml:mo>×</mml:mo> </mml:mrow> </mml:math> improvement on communication compared to the state-of-the-art implementation. Its end-to-end running time is at least $$70\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>70</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> faster in a 10Mbps network. Using the recent results on compressed $$\varSigma $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>Σ</mml:mi> </mml:math> -protocol theory, we obtain a discrete-log-based constant-round zero-knowledge argument with $$\mathcal {O}(C^{1/2})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mo>(</mml:mo> <mml:msup> <mml:mi>C</mml:mi> <mml:mrow> <mml:mn>1</mml:mn> <mml:mo>/</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> communication and common random string length, improving over the state of the art that has linear-size common random string and requires heavier computation. We improve the communication of a designated n -verifier zero-knowledge proof from $$\mathcal {O}(nC/B+n^2B^2)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mo>(</mml:mo> <mml:mi>n</mml:mi> <mml:mi>C</mml:mi> <mml:mo>/</mml:mo> <mml:mi>B</mml:mi> <mml:mo>+</mml:mo> <mml:msup> <mml:mi>n</mml:mi> <mml:mn>2</mml:mn> </mml:msup> <mml:msup> <mml:mi>B</mml:mi> <mml:mn>2</mml:mn> </mml:msup> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> to $$\mathcal {O}(nC/B+n^2)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mo>(</mml:mo> <mml:mi>n</mml:mi> <mml:mi>C</mml:mi> <mml:mo>/</mml:mo> <mml:mi>B</mml:mi> <mml:mo>+</mml:mo> <mml:msup> <mml:mi>n</mml:mi> <mml:mn>2</mml:mn> </mml:msup> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> . To demonstrate the scalability of our compilers, we were able to extract a commit-and-prove SIMD ZK from Ligero and cast it in our framework. We also give one instantiation derived from LegoSNARK, demonstrating that the idea of CP-SNARK also fits in our methodology.

Liang Zhang, Zhanrong Ou, Changhui Hu, Haibin Kan · 5 authors

Data sharing is ubiquitous in the metaverse, which adopts blockchain as its foundation. Blockchain is employed because it enables data transparency, achieves tamper resistance, and supports smart contracts. However, securely sharing data based on blockchain necessitates further consideration. Ciphertext-policy attribute-based encryption (CP-ABE) is a promising primitive to provide confidentiality and fine-grained access control. Nonetheless, authority accountability and key abuse are critical issues that practical applications must address. Few studies have considered CP-ABE key confidentiality and authority accountability simultaneously. To our knowledge, we are the first to fill this gap by integrating non-interactive zero-knowledge (NIZK) proofs into CP-ABE keys and outsourcing the verification process to a smart contract. To meet the decentralization requirement, we incorporate a decentralized CP-ABE scheme into the proposed data sharing system. Additionally, we provide an implementation based on smart contract to determine whether an access control policy is satisfied by a set of CP-ABE keys. We also introduce an open incentive mechanism to encourage honest participation in data sharing. Hence, the key abuse issue is resolved through the NIZK proof and the incentive mechanism. We provide a theoretical analysis and conduct comprehensive experiments to demonstrate the feasibility and efficiency of the data sharing system. Based on the proposed accountable approach, we further illustrate an application in GameFi, where players can play to earn or contribute to an accountable DAO, fostering a thriving metaverse ecosystem.

Yanming Zhu, Xuefei Yin, Alan Wee‐Chung Liew, Hui Tian

With the rapid advancement of artificial intelligence and deep learning, medical image analysis has become a critical tool in modern healthcare, significantly improving diagnostic accuracy and efficiency. However, AI-based methods also raise serious privacy concerns, as medical images often contain highly sensitive patient information. This review offers a comprehensive overview of privacy-preserving techniques in medical image analysis, including encryption, differential privacy, homomorphic encryption, federated learning, and generative adversarial networks. We explore the application of these techniques across various medical image analysis tasks, such as diagnosis, pathology, and telemedicine. Notably, we organizes the review based on specific challenges and their corresponding solutions in different medical image analysis applications, so that technical applications are directly aligned with practical issues, addressing gaps in the current research landscape. Additionally, we discuss emerging trends, such as zero-knowledge proofs and secure multi-party computation, offering insights for future research. This review serves as a valuable resource for researchers and practitioners and can help advance privacy-preserving in medical image analysis.

P. Surendar, Adhithi Satish Kumar, Venkata Pavani, M. Azhagiri

No abstract is available for this record.

Benny Applebaum, Benny Pinkas

No abstract is available for this record.

Dan Boneh

What will computer security look like in the year 2100? This talk will begin with a few predictions that aim to suggest a few research directions in the present. We will then transition to the exciting area of applied zero knowledge proofs, an area that has seen tremendous growth in recent years. We will describe some of the new ideas in the space and focus on a number of remarkable real-word applications of these techniques. The talk will be self contained and accessible to all.

Alex Berke, Tobin South, Robert Mahari, Kent Larson · 5 authors

Tax returns contain financial information of interest to third parties: public officials are asked to share financial data for transparency, companies seek to assess the financial status of business partners, and individuals need to prove their income to third-parties.Tax returns also contain sensitive data such that sharing them in their entirety undermines privacy.We outline how zero-knowledge cryptography may be applied to address this tension by allowing individuals and organizations to make provable claims about select information in their tax returns without revealing additional information, in a way that can be independently verified by third parties.We highlight key system goals and design specifications for this zero-knowledge tax disclosure system (zkTax) and present a prototype implementation.The prototype consists of three distinct services that can be distributed: a tax authority that provides signed tax documents; a Redact & Prove Service that enables users to redact tax documents and produce a zero-knowledge proof attesting the provenance of the redacted data; and a Verify Service to check the validity of claims.We demonstrate how zkTax could be implemented with minimal changes to existing tax infrastructure, allowing the system to be extensible to other contexts and jurisdictions.This work provides a practical example of how distributed tools leveraging cryptography can enhance existing government or financial infrastructures, providing immediate transparency alongside privacy without system overhauls.

Ghazaleh Keshavarzkalhori, Cristina Pérez‐Solà, Guillermo Navarro‐Arribas, Jordi Herrera‐Joancomartí

In many machine learning scenarios, training occurs outside the control of the model sponsor or the entity using the model. A growing concern in such settings revolves around model poisoning and data poisoning-how training is conducted and which data contributes to the process. This paper introduces a protective scheme against model and data poisoning attacks. Leveraging cryptographic primitives such as hashes, signature schemes, and zero-knowledge proofs, the scheme ensures the integrity of the training process. Hashing maintains the continuity of data from authenticated sensors, while signatures validate the data. In the end, zero-knowledge proofs verify the correct model computation by the entity carrying out the training process. By adopting this approach, model sponsors can securely delegate training tasks, guaranteeing the authenticity of the results. Implementation and testing demonstrate the scheme's feasibility, effectively countering data and model poisoning threats.

Yibin Yang, David Heath, Carmit Hazay, Vladimir Kolesnikov · 5 authors

We explore Zero-Knowledge Proofs (ZKPs) of statements expressed as programs written in high-level languages, e.g., C or assembly. At the core of executing such programs in ZK is the repeated evaluation of a CPU step, achieved by branching over the CPU's instruction set. This approach is general and covers traversal-execution of a program's control flow graph (CFG): here CPU instructions are straight-line program fragments (of various sizes) associated with the CFG nodes. This highlights the usefulness of ZK CPUs with a large number of instructions of varying sizes.

Ny Hasina Andriambelo, Naghmeh Moradpoor

The rise of Autonomous Vehicles (AVs) brings with it the need for secure and privacy-preserving machine learning models. Federated Learning (FL) allows AVs to collaboratively train models while keeping raw data localized. However, traditional FL systems are vulnerable to security threats, including adversarial attacks, data breaches, and dependency on a central aggregator, which can be a single point of failure. To address these concerns, this paper introduces a peer-to-peer decentralized federated learning system that integrates lightweight blockchain technology and Binius Zero- Knowledge Proofs (ZKPs) to enhance security and privacy. In this system, Binius ZKPs ensure that model updates are cryptographically verified without exposing sensitive information, guaranteeing data confidentiality and integrity during the learning process. The lightweight blockchain framework secures the network by creating an immutable, decentralized record of all model updates, thus preventing tampering, fraud, or unauthorized modifications. This decentralized approach eliminates the need for a central aggregator, significantly enhancing system resilience to attacks and making it suitable for dynamic environments like AV networks. Additionally, the system's design includes Byzantine resilience, providing protection against adversarial nodes and ensuring that the global model aggregation process remains robust even in the presence of malicious actors. Extensive performance evaluations demonstrate that the system achieves low-latency, scalability, and efficient resource usage while maintaining strong security and privacy guarantees, making it an ideal solution for real-time federated learning in autonomous vehicle networks. The proposed framework not only ensures privacy but also fosters trust among participants in a fully decentralized environment.

Guofeng Tang, Shuai Han, Li Lin, Changzheng Wei · 5 authors

With the demand of cryptocurrencies, threshold ECDSA recently regained popularity. So far, several methods have been proposed to construct threshold ECDSA, including the usage of OT and homomorphic encryptions (HE). Due to the mismatch between the plaintext space and the signature space, HE-based threshold ECDSA always requires zero-knowledge range proofs, such as Paillier and Joye-Libert (JL) encryptions. However, the overhead of range proofs constitutes a major portion of the total cost.

Ron Steinfeld, Amin Sakzad, Muhammed F. Esgin, Veronika Kuchta · 6 authors

We introduce the first candidate Lattice-based designated verifier (DV) zero knowledge sUccinct Non-interactive Argument (ZK-SNARG) protocol, named LUNA, with quasi-optimal proof length (quasi-linear in the security/privacy parameter). By simply relying on mildly stronger security assumptions, LUNA is also a candidate ZK-SNARK (i.e. argument of knowledge). LUNA achieves significant improvements in concrete proof sizes, reaching below 6 KB (compared to >32 KB in prior work) for 128-bit security/privacy level. To achieve our quasi-optimal succinct LUNA, we give a new regularity result for 'private' re-randomization of Module LWE (MLWE) samples using discrete Gaussian randomization vectors, also known as a lattice-based leftover hash lemma with leakage, which applies with a discrete Gaussian re-randomization parameter that is polynomial in the statistical privacy parameter (avoiding exponential smudging), and hides the coset of the re-randomization vector support set. Along the way, we derive bounds on the smoothing parameter of the intersection of short integer solution (SIS), gadget, and Gaussian perp module lattices over the power of 2 cyclotomic rings. We then introduce a new candidate linear-only homomorphic encryption scheme called Module Half-GSW (HGSW), and apply our regularity theorem to provide smudging-free circuit-private homomorphic linear operations for Module HGSW. Our implementation and experimental performance evaluation show that, for typical instance sizes, Module HGSW provides favourable performance for ZK-SNARG applications involving lightweight verifiers. It enables significantly (around 5x) shorter proof lengths while speeding up CRS generation and encryption time by 4-16x and speeding up decryption time by 4.3x, while incurring just 1.2-2x time overhead in linear homomorphic proof generation operations, compared to a Regev encryption used in prior work in the ZK-SNARG context. We believe our techniques are of independent interest and will find application in other privacy-preserving lattice-based protocols.

Michael L. Rosenberg, Tushar Mopuri, Hossein Hafezi, Ian Miers · 5 authors

Zero-knowledge Succinct Non-interactive ARguments of Knowledge (zkSNARKs) allow a prover to convince a verifier of the correct execution of a large computation in private and easily-verifiable manner.These properties make zkSNARKs a powerful tool for adding accountability, scalability, and privacy to numerous systems such as blockchains and verifiable key directories.Unfortunately, existing zkSNARKs are unable to scale to large computations due to time and space complexity requirements for the prover algorithm.As a result, they cannot handle real-world instances of the aforementioned applications.In this work, we introduce Hekaton, a zkSNARK that overcomes these barriers and can efficiently handle arbitrarily large computations.We construct Hekaton via a new "distribute-and-aggregate" framework that breaks up large computations into small chunks, proves these chunks in parallel in a distributed system, and then aggregates the resulting chunk proofs into a single succinct proof.Underlying this framework is a new technique for efficiently handling data that is shared between chunks that we believe could be of independent interest.We implement a distributed prover for Hekaton, and evaluate its performance on a compute cluster.Our experiments show that Hekaton achieves strong horizontal scalability (proving time decreases linearly as we increase the number of nodes in the cluster), and is able to prove large computations quickly: it can prove computations of size 2 35 gates in under an hour, which is much faster than prior work.Finally, we also apply Hekaton to two applications of realworld interest: proofs of batched insertion for a verifiable key directory and proving correctness of RAM computations.In both cases, Hekaton is able to scale to handle realistic workloads with better efficiency than prior work.

Christodoulos Pappas, Dimitrios Papadopoulos

Space-efficient SNARKs aim to reduce the prover's space overhead which is one the main obstacles for deploying SNARKs in practice, as it can be prohibitively large (e.g., orders of magnitude larger than natively performing the computation). In this work, we propose Sparrow, a novel space-efficient zero-knowledge SNARK for data-parallel arithmetic circuits with two attractive features: (i) it is the first space-efficient scheme where, for a given field, the prover overhead increases with a multiplicative sublogarithmic factor as the circuit size increases, and (ii) compared to prior space-efficient SNARKs that work for arbitrary arithmetic circuits, it achieves prover space asymptotically smaller than the circuit size itself. Our key building block is a novel space-efficient sumcheck argument with improved prover time which may be of independent interest. Our experimental results for three use cases (arbitrary data parallel circuits, multiplication trees, batch SHA256 hashing) indicate Sparrow outperforms the prior state-of-the-art space-efficient SNARK for arithmetic circuits Gemini (Bootle et al., EUROCRYPT'22) by 3.2-28.7x in total prover space and 3.1-11.3x in prover time. We then use Sparrow to build zero-knowledge proofs of tree training and prediction, relying on its space efficiency to scale to large datasets and forests of multiple trees. Compared to a (non-space-efficient) optimal-time SNARK based on the GKR protocol, we observe prover space reduction of 16-240x for tree training while maintaining essentially the same prover and verifier times and proof size. Even more interestingly, our prover requires comparable space to natively perform the underlying computation. E.g., for a 400MB dataset, our prover only needs 1.4x more space than the native computation.

Evan Laufer, Alex Ozdemir, Dan Boneh

Interactive theorem provers (ITPs), such as Lean and Coq, can express formal proofs for a large category of theorems, from abstract math to software correctness. Consider Alice who has a Lean proof for some public statement T. Alice wants to convince the world that she has such a proof, without revealing the actual proof. Perhaps the proof shows that a secret program is correct or safe, but the proof itself might leak information about the program's source code. A natural way for Alice to proceed is to construct a succinct, zero-knowledge, non-interactive argument of knowledge (zkSNARK) to prove that she has a Lean proof for the statement T.

Vadim Lyubashevsky, Gregor Seiler, Patrick Steuer

The hardness of lattice problems offers one of the most promising security foundations for quantum-safe cryptography. Basic schemes for public key encryption and digital signatures are already close to standardization at NIST and several other standardization bodies, and the research frontier has moved on to building primitives with more advanced privacy features. At the core of many such primitives are zero-knowledge proofs. In recent years, zero-knowledge proofs for (and using) lattice relations have seen a dramatic jump in efficiency and they currently provide arguably the shortest, and most computationally efficient, quantum-safe proofs for many scenarios. The main difficulty in using these proofs by non-experts (and experts!) is that they have a lot of moving parts and a lot of internal parameters depend on the particular instance that one is trying to prove.

Daniel Escudero, Antigoni Polychroniadou, Yifan Song, Chenkai Weng

In this work we study the efficiency of Zero-Knowledge (ZK) arguments of knowledge, particularly exploring Multi-Verifier ZK (MVZK) protocols as a midway point between Non-Interactive ZK and Designated-Verifier ZK, offering versatile applications across various domains. We introduce a new MVZK protocol designed for the preprocessing model, allowing any constant fraction of verifiers to be corrupted, potentially colluding with the prover. Our contributions include the first MVZK over rings. Unlike recent prior works on fields in the dishonest majority case, our protocol demonstrates communication complexity independent of the number of verifiers, contrasting the linear complexity of previous approaches. This key advancement ensures improved scalability and efficiency. We provide an end-to-end implementation of our protocol. The benchmark shows that it achieves a throughput of 1.47 million gates per second for 64 verifiers with 50% corruption, and 0.88 million gates per second with 75% corruption.

S. Bharath Babu, K R Jothi

In the realm of healthcare analytics, preserving the privacy of sensitive data while enabling valuable insights poses a significant challenge, particularly given the increasing prevalence of data breaches and the sensitivity of personal health information. This paper presents a secure framework that addresses these concerns by integrating privacy-preserving parameters, zero-knowledge proofs (zk-SNARKs), blockchain technology, and a multi-tenant cloud environment. Through advanced cryptographic techniques, specifically zk-SNARKs, the framework ensures that healthcare records remain protected during analytics computations, without exposing raw data. The privacy-preserving analytics engine utilizes anonymized healthcare records and generates zk-SNARKs to validate computations. These proofs, integrated into a blockchain network, create a tamper-proof, transparent ledger that ensures secure healthcare transactions. This approach is critical in scenarios such as telemedicine, where secure data sharing and computation are paramount. By demonstrating its application in a telemedicine app, the framework highlights its practical significance in balancing data utility and privacy in healthcare analytics, providing a scalable and secure solution to a pressing problem.