Sourav Das, Philippe Camacho, Zhuolun Xiang, J Sierra Nieto · 6 authors
Threshold signatures protect the signing key by sharing it among a group of signers so that an adversary must corrupt a threshold number of signers to be able to forge signatures. Existing threshold signatures with succinct signatures and constant verification times do not work if signers have different weights. Such weighted settings are seeing increasing importance in decentralized systems, especially in the Proof-of-Stake blockchains. This paper presents a new paradigm for threshold signatures for pairing and discrete logarithm-based cryptosystems. Our scheme has a compact verification key consisting of only 7 group elements, and a signature consisting of 8 group elements. Verifying the signature requires 8 exponentiations and 8 bilinear pairings. Our scheme supports arbitrary weight distributions among signers and arbitrary thresholds. It requires non-interactive preprocessing after a universal powers-of-tau setup. We prove the security of our scheme in the Algebraic Group Model and implement it using Golang. Our evaluation shows that our scheme achieves a comparable signature size and verification time to a standard (unweighted) threshold signature. Compared to existing multisignature schemes, our scheme has a much smaller public verification key.
We study the problem of committee selection in the context of proof-of-stake consensus mechanisms or distributed ledgers. These settings determine a family of participating parties---each of which has been assigned a non-negative ''stake''---and are subject to an adversary that may corrupt a subset of the parties. The challenge is to select a committee of participants that accurately reflects the proportion of corrupt and honest parties, as measured by stake, in the full population. The trade-off between committee size and the probability of selecting a committee that over-represents the corrupt parties is a fundamental factor in both security and efficiency of proof-of-stake consensus, as well as committee-run layer-two protocols.
Web3 applications based on blockchains regularly need access to randomness that is unbiased, unpredictable, and publicly verifiable. For Web3 gaming applications, this becomes a crucial selling point to attract more users by providing credibility to the "random reward" distribution feature. A verifiable random function (VRF) protocol satisfies these requirements naturally, and there is a tremendous rise in the use of VRF services. As most blockchains cannot maintain the secret keys required for VRFs, Web3 applications interact with external VRF services via a smart contract where a VRF output is exchanged for a fee. While this smart contract-based plain-text exchange offers the much-needed public verifiability immediately, it severely limits the way the requester can employ the VRF service: the requests cannot be made in advance, and the output cannot be reused. This introduces significant latency and monetary overhead.
How can a model owner prove they trained their model according to the correct specification? More importantly, how can they do so while preserving the privacy of the underlying dataset and the final model? We study this problem and formulate the notion of zero-knowledge proof of training (zkPoT), which formalizes rigorous security guarantees that should be achieved by a privacy-preserving proof of training. While it is theoretically possible to design zkPoT for any model using generic zero-knowledge proof systems, this approach results in extremely unpractical proof generation times. Towards designing a practical solution, we propose the idea of combining techniques from MPC-in-the-head and zkSNARKs literature to strike an appropriate trade-off between proof size and proof computation time. We instantiate this idea and propose a concretely efficient, novel zkPoT protocol for logistic regression.
Privacy is one of the essential pillars for the widespread adoption of blockchains, but public blockchains are transparent by nature. Modern analytics techniques can easily subdue the pseudonymity feature of a blockchain user. Some applications have been able to provide practical privacy protections using privacy-preserving cryptography techniques. However, malicious actors have abused them illicitly, discouraging honest actors from using privacy-preserving applications as "mixing" user interactions and funds with anonymous bad actors, causing compliance and regulatory concerns. In this paper, we propose a framework that balances privacy-preserving features by establishing a regulatory and compliant framework called Selective De-Anonymization (SeDe). The adoption of this framework allows privacy-preserving applications on blockchains to de-anonymize illicit transactions by recursive traversal of subgraphs of linked transactions. Our technique achieves this without leaving de-anonymization decisions or control in the hands of a single entity but distributing it among multiple entities while holding them accountable for their respective actions. To instantiate, our framework uses threshold encryption schemes and Zero-Knowledge Proofs (ZKPs).
Blockchain technology has garnered significant attention from global organizations and researchers due to its potential as a solution for centralized system challenges. Concurrently, the Internet of Things (IoT) has revolutionized the Fourth Industrial Revolution by enabling interconnected devices to offer innovative services, ultimately enhancing human lives. This paper presents a new approach utilizing lightweight blockchain technology, effectively reducing the computational burden typically associated with conventional blockchain systems. By integrating this lightweight blockchain with IoT systems, substantial reductions in implementation time and computational complexity can be achieved. Moreover, the paper proposes the utilization of the Okamoto Uchiyama encryption algorithm, renowned for its homomorphic characteristics, to reinforce the privacy and security of IoT-generated data. The integration of homomorphic encryption and blockchain technology establishes a secure and decentralized platform for storing and analyzing sensitive data of the supply chain data. This platform facilitates the development of some business models and empowers decentralized applications to perform computations on encrypted data while maintaining data privacy. The results validate the robust security of the proposed system, comparable to standard blockchain implementations, leveraging the distinctive homomorphic attributes of the Okamoto Uchiyama algorithm and the lightweight blockchain paradigm.
A pivotal feature of IPv6 is its plug-and-play capability that enables hosts to integrate seamlessly into networks. In the absence of a trusted authority or security infrastructure, the challenge for hosts is generating their own address and verifying ownership of others. Cryptographically Generated Addresses (CGA) solves this problem by binding IPv6 addresses to hosts' public keys to prove address ownership. CGA generation involves solving a cryptographic puzzle similar to Bitcoin's Proof-of-Work (PoW) to deter address spoofing. Unfortunately, solving the puzzle often causes undesirable address generation delays, which has hindered the adoption of CGA. In this paper, we present Bitcoin-Certified Addresses (BCA), a new technique to bind IPv6 addresses to hosts' public keys. BCA reduces the computational cost of generating addresses by using the PoW computed by Bitcoin nodes to secure the binding. Compared to CGA, BCA provides better protection against spoofing attacks and improves the privacy of hosts. Due to the decentralized nature of the Bitcoin network, BCA avoids reliance on a trusted authority, similar to CGA. BCA shows how the PoW computed by Bitcoin nodes can be reused, which saves costs for hosts and makes Bitcoin mining more efficient.
Nidhi Desai, Damiano Di Francesco Maesa, Nishanth Sastry, Steve Schneider · 5 authors
This paper is concerned with helping people who are vulnerable during important transitions in life, such as 'coming out' as LGBTQIA+, experiencing serious illness, undergoing relationship breakdown etc. Rich sensor streams derived from so-called 'smart' Internet of Things (IoT) devices can be highly beneficial, for example in ensuring the safety of such individuals during their sensitive life transitions, or in providing functionality that can mitigate some of the difficulties faced by them. However, the data that needs to be extracted to provide these benefits can itself be highly sensitive and needs to be processed with safeguards to protect privacy. We develop scenarios that highlight issues arising from having to merge data streams from multiple devices, including data governance issues that are relevant when the sensors are owned by multiple individuals. We propose a "Transition Guardian" architecture that leverages "Smart Experts" written as smart contracts operating on homomorphically encrypted sensor data streams to provide real-time protection without disclosing their sensitive information. We have also implemented a proof-of-concept on the Ethereum protocol to validate our proposed solution.
Decentralized physical infrastructure network (DePIN) is an emerging research topic in Web3 and blockchain. By combining blockchain, IoT and tokenomics, DePINs are expected to disrupt existing IoT business models and enable Web3 communities to build innovative, machine-driven and decentralized IoT networks and applications. Due to the characteristics of DePINs such as the large number of smart devices and network scale as well as the interactions with blockchain, scalability remains to be one of the key challenges. In this position paper, we outline the core ideas and components for building a rollup-centric scalable architecture for DePINs. The proposed architecture takes a modular design approach and leverages off-chain computing and zero-knowledge proofs to address the scalability challenge. This work is expected to highlight the importance of this new research direction and shed some light on potential solutions.
This paper proposes a novel recursive polynomial commitment scheme (PCS) and a new polynomial interactive oracle proof (PIOP) protocol, which compile into efficient and transparent zk-SNARKs (zero-knowledge succinct non-interactive arguments of knowledge). The Ethereum blockchain utilizes zero-knowledge Rollups (ZKR) to improve its scalability (the ability to handle a large number of transactions), and ZKR uses zk-SNARKs to validate transactions. The currently used zk-SNARKs rely on a trusted setup ceremony, where a group of participants uses secret information about transactions to generate the public parameters necessary to verify the zk-SNARKs. This introduces a security risk into Ethereum's system. Thus, researchers have been developing transparent zk-SNARKs (which do not require a trusted setup), but those are not as efficient as non-transparent zk-SNARKs, so ZKRs do not use them. In this research, I developed LUMEN, a set of novel algorithms that generate transparent zk-SNARKs that improve Ethereum's security without sacrificing its efficiency. Various techniques were creatively incorporated into LUMEN, including groups with hidden orders, Lagrange basis polynomials, and an amortization strategy. I wrote mathematical proofs for LUMEN that convey its completeness, soundness and zero-knowledgeness, and implemented LUMEN by writing around $8000$ lines of Rust and Python code, which conveyed the practicality of LUMEN. Moreover, my implementation revealed the efficiency of LUMEN (measured in proof size, proof computation time, and verification time), which surpasses the efficiency of existing transparent zk-SNARKs and is on par with that of non-transparent zk-SNARKs. Therefore, LUMEN is a promising solution to improve Ethereum's security while maintaining its efficiency.
We propose a secure, stateless and composable transaction scheme to establish delivery-versus-payment (DvP) across two (or more) blockchains without relying on time-locks, centralized escrow, or stateful intermediaries. The method minimizes coordination overhead and removes race conditions via a stateless decryption oracle that conditionally releases cryptographic keys. Specifically, the scheme requires: 1) a decryption oracle service (either centralized or using threshold decryption) that decrypts transaction-specific encrypted messages, and 2) a payment contract on the payment chain that executes conditional payments via transferAndDecrypt and emits the appropriate key, depending on transaction outcome (finality). The decrypted key then deterministically enables follow-up transactions - such as asset delivery or cancellation - on a separate blockchain. The protocol is lightweight and compatible with existing blockchain infrastructure (e.g., Ethereum), and avoids timeouts or pre-defined orderings. Our approach improves atomic cross-chain settlement and can serve as a blueprint for decentralized inter-chain financial markets. The protocol allows for multi-party DvP across multiple chains. A multi-party delivery versus payment is a valuable trade feature as it allows to bound multiple trade into a single atomic unit, effectively reducing liquidity requirements.
We provide a novel smart contract-based Verifiable Random Function (VRF) scheme that addresses the challenges associated with existing frameworks. Our suggested distributed VRF instantiation employs multi-party computation (MPC) within a blockchain network, ensuring collective randomness and security by preventing any singular participant from predicting the VRF’s output. We endeavor to augment the stochastic nature of our VRF system by incorporating a cryptographically secure ChaCha20 sequence for the generation of pseudo-random sequences. We consider the forward security by adapting Identity-based Encryption (IBE) and show a cryptographic VRF construction, based on Elgamal Encryption and Discrete Logarithm Problem (DLP)-based cryptographic primitives with Decentralized Identifier (DID) for the MPC operations. The construction presents how to prove the correctness of the VRF’s outputs with proofs, rendering it applicable for use cases necessitating random yet verifiable values. Moreover, we show the security analysis with formal proofs and entropy approximation for the randomness of the VRF output. Furthermore, employing the NIST SP800-22 randomness test suite for statistical randomness evaluation, our result shows an overall pass rate of 96.59% across a total of 176 tests encompassing 11 standard test cases. The average p -value is observed as 0.5728, indicative of relevant statistical randomness within the generated sequences. We also provide specific details on the implementation of our VRF scheme within a Solidity smart contract.
The integration of cryptographic algorithms like Advanced Encryption Standard (AES) and Elliptic Curve Cryptography (ECC) is pivotal in bolstering the core attributes of blockchain technology, especially in achieving decentralization, tamper resistance, and anonymization within the realm of medical applications. Despite their widespread utilization, the conventional AES and ECC face significant hurdles in security and efficiency when dealing with expansive medical data, posing a challenge to the effective preservation of patient privacy. In light of these challenges, this study introduces HAE (hybrid AES and ECC), an innovative hybrid cryptographic algorithm that ingeniously amalgamates the robustness of AES with the agility of ECC. HAE is designed to symmetrically encrypt original data with AES while employing ECC for the asymmetric encryption of the initial AES key. This strategy not only alleviates the complexities associated with AES key management but also enhances the algorithm’s security without compromising its efficiency. We provide an in-depth exposition of HAE’s deployment within a framework tailored for medical scenarios, offering empirical insights into its enhanced performance metrics. Our experimental outcomes underscore HAE’s exemplary security, time efficiency, and optimized resource consumption, affirming its potential as a breakthrough advancement for augmenting blockchain applications in the medical sector, heralding a new era of enhanced data security and privacy within this critical domain.
A redactable blockchain allows authorized individuals to remove or replace undesirable content, offering the ability to remove illegal or unwanted information. Access control is a mechanism that limits data visibility and ensures that only authorized users can decrypt and access encrypted information, playing a crucial role in addressing privacy concerns and securing the data stored on a blockchain. Redactability and access control are both essential components when implementing a regulated consortium blockchain in real-world situations to ensure the secure sharing of data while removing undesirable content. We propose a decentralized consortium blockchain system prototype that supports redactability and access control. Through the development of a prototype blockchain system, we investigate the feasibility of combining these approaches and demonstrate that it is possible to implement a redactable blockchain with access control in a consortium blockchain setting.
Abstract In this paper we introduce a scalable, privacy-preserving, federated learning framework, coined FLoBC, based on the concept of distributed ledgers underlying blockchains. This is motivated by the rapid growth of data worldwide, especially decentralized data which calls for scalable, decenteralized machine learning models which is capable of preserving the privacy of the data of the participating users. Towards this objective, we first motivate and define the problem scope. We then introduce the proposed FLoBC system architecture hinging on a number of key pillars, namely parallelism, decentralization and node update synchronization. In particular, we examine a number of known node update synchronization policies and examine their performance merits and design trade-offs. Finally, we compare the proposed federated learning system to a centralized learning system baseline to demonstrate its performance merits. Our main finding in this paper is that our proposed decentralized learning framework was able to achieve comparable performance to a classic centralized learning system, while distributing the model training process across multiple nodes without sharing their actual data. This provides a scalable, privacy-preserving solution for training a variety of large machine learning models. Graphical abstract
Md Jahangir Alam, Ismail Hossain, Sai Puppala, Sajedul Talukder
Identity attacks, such as impersonation, identity theft, and fraudulent account creation, pose significant threats to the security and trustworthiness of Online Social Networks (OSNs). In this paper, we propose a robust and secure framework to verify user identities without compromising their privacy by developing a multi-layered framework leveraging zero-knowledge proof (ZKP) and Hyperledger Fabric private blockchain. We introduce a blockchain-based government identity provider system, coupled with a zero-knowledge proof-based signup process for social networks. Our prototype authenticates user identities in multiple layers, effectively mitigating fraudulent, cloned, and multiple account creations. Our experiments with n (n = 50) users showed a 100% success rate for our system, highlighting its effectiveness compared to other OSNs.
Abstract Blockchain technology has gained widespread adoption in recent years due to its ability to enable secure and transparent record-keeping and data transfer. A critical aspect of blockchain technology is the use of consensus algorithms, which allow distributed nodes in the network to agree on the state of the blockchain. In this review paper, we examine various consensus algorithms that are used in blockchain systems, including proof-of-work, proof-of-stake, and hybrid approaches. We go over the trade-offs and factors to think about when choosing a consensus algorithm, such as energy efficiency, decentralization, and security. We also look at the strengths and weaknesses of each algorithm as well as their potential impact on the scalability and adoption of blockchain technology.
Engin Zeydan, Luis Blanco, Josep Mangues, Şuayb S. Arslan · 5 authors
Self-Sovereign Identity (SSI) has emerged lately as an identity and access management framework that is based on Distributed Ledger Technology (DLT) and allows users to control their own data. Federate Learning (FL), on the other hand, provides a framework to update Machine Learning (ML) models without relying on explicit data exchange between the users. This paper investigates identity management and authentication for vehicle users, which are participating into FL. We propose a new approach to SSI, that is alternative to the conventional blockchain-based SSI, specifically for use in vehicular networks, which focuses on maintaining confidentiality, authenticity, and integrity of vehicle users' identities and data exchanged between the users and the aggregation server during the execution of the FL process. We also provide experimental results for distributed identity management (DIM) operations, which show that the performance of credential operations in the implemented system is generally efficient and the average times are within reasonable limits. However, there is a slight increase in presentation time, offer time, connection establishment time, and credential revocation time as the number of requests increases, indicating a slight degradation in performance for these operations.
In today's world, secure and efficient biometric authentication is of keen importance. Traditional authentication methods are no longer considered reliable due to their susceptibility to cyber-attacks. Biometric authentication, particularly fingerprint authentication, has emerged as a promising alternative, but it raises concerns about the storage and use of biometric data, as well as centralized storage, which could make it vulnerable to cyber-attacks. In this paper, a novel blockchain-based fingerprint authentication system is proposed that integrates zk-SNARKs, which are zero-knowledge proofs that enable secure and efficient authentication without revealing sensitive biometric information. A KNN-based approach on the FVC2002, FVC2004 and FVC2006 datasets is used to generate a cancelable template for secure, faster, and robust biometric registration and authentication which is stored using the Interplanetary File System. The proposed approach provides an average accuracy of 99.01%, 98.97% and 98.52% over the FVC2002, FVC2004 and FVC2006 datasets respectively for fingerprint authentication. Incorporation of zk-SNARK facilitates smaller proof size. Overall, the proposed method has the potential to provide a secure and efficient solution for blockchain-based identity management.
<title>Abstract</title> Blockchain technology has gained significant attention in recent years due to its decentralized and secure nature. Ethereum a popular blockchain platform supports the execution of smart contracts and enables the storage of data on the blockchain. However Ethereum's public nature may not be suitable for scenarios where sensitive or private data needs to be stored and accessed securely. In this paper we present GANACHE an efficient private Ethereum blockchain tool that addresses the confidentiality and security concerns associated with data storage. GANACHE leverages various cryptographic techniques such as encryption and zero-knowledge proofs to ensure the privacy of stored data while maintaining the benefits of blockchain technology.
Open access
Blockchain Technology Applications and Security
Cryptography and Data Security
Advanced Steganography and Watermarking Techniques
The ERC721 standard defines a Non-Fungible Token (NFT) as an identifier that uniquely identifies digital data recorded on a blockchain. The NFT currently in use claim to ensure the uniqueness of the contents associated with the NFT by taking advantage of the tamper-resistant characteristics of the blockchain data. Besides, digital signatures based on public-key encryption is a representative technique to prevent digital data from being falsified, and its application to NFT is also discussed. In this study, we first consider an NFT implementation by applying a designated confirmer signature using an interactive verification method without using blockchain technology. We design an NFT issuance protocol that guarantees the uniqueness of data with a designated confirmer signature, and evaluate the security of the protocol. Moreover, we compare it with existing blockchain implementations. By analyzing the both methods, we aim to provide insights into the potential applications and performance in using designated confirmer signatures for NFT. Our findings contribute to the ongoing research on secure and efficient mechanisms for ensuring the integrity and uniqueness of digital assets in decentralized systems.
Carlo Mazzocca, Nicolò Romandini, Rebecca Montanari, Paolo Bellavista
The proliferation of Internet of Things (IoT) devices, generating massive amounts of heterogeneous distributed data, has pushed toward edge cloud computing as a promising paradigm to bring cloud capabilities closer to data sources. In many cases of practical interest, centralized Machine Learning (ML) approaches can hardly be employed due to high communication costs, low reliability, legal restrictions, and scalability issues. Therefore, Federated Learning (FL) is emerging as a promising distributed ML approach that enables models to be trained on remote devices using their local data. However, “traditional” FL solutions still present open technical challenges, such as single points of failure and lack of trustworthiness among participants. To address these open challenges, some researchers have started to propose leveraging blockchain technologies. However, the adoption of blockchain for FL at the edge is limited by several factors nowadays, such as long waiting times for transaction confirmation and high energy consumption. In this work, we conduct an original and comprehensive analysis of the key design challenges to address towards an efficient implementation of FL at the edge, and analyze how Distributed Ledger Technologies (DLTs) can be employed to overcome them. Then, we present a novel architecture that enables FL at the edge by leveraging the IOTA Tangle, a next-generation DLT whose data structure is a directed acyclic graph (DAG), and the InterPlanetary File System (IPFS) to store and share partial models. Experimental results demonstrate the feasibility and efficiency of our proposed solution in real-world deployment scenarios.
We propose a trustless blockchain-based protocol for secure and decentralized generation of secret random numbers. In our protocol, which can be implemented as a smart contract, a client Cassie can ask for a secret random number$r$. The protocol will then allow anyone on the blockchain network to contribute to the process of generating$r$, but crucially, only Cassie would be able to see the final result. Additionally, our approach is auditable, i.e. if Cassie later wishes to announce her secret random number$r$, she can prove that the output of the process was indeed the claimed value. Finally, we ensure no one else is able to deduce$r$before Cassiediscloses it. A primary use-case of this approach is to enable auditable online gambling and casinos with verifiable and tamper-proof randomness. If Cassie is a casino, she can ask for a secret random number$r$at the beginning of the day, and then use it as a seed in a predefined pseudo-random number generator which is in turn used as the source of randomness for all bets during the day. At the end of the day, Cassie can prove that the seed was indeed generated by our decentralized protocol. Thus, she can prove that the casino had no control over the outcomes of the bets. Moreover, this is a cost- and time-efficient approach as it does not require us to generate a new random number for every bet.