We propose three constructions of classically verifiable non-interactive proofs (CV-NIP) and non-interactive zero-knowledge proofs and arguments (CV-NIZK) for QMA in various preprocessing models.
- We construct an information theoretically sound CV-NIP for QMA in the secret parameter model where a trusted party generates a quantum proving key and classical verification key and gives them to the corresponding parties while keeping it secret from the other party. Alternatively, we can think of the protocol as one in a model where the verifier sends an instance-independent quantum message to the prover as preprocessing.
- We construct a CV-NIZK for QMA in the secret parameter model. It is information theoretically sound and zero-knowledge.
- Assuming the quantum hardness of the leaning with errors problem, we construct a CV-NIZK for QMA in a model where a trusted party generates a CRS and the verifier sends an instance-independent quantum message to the prover as preprocessing. This model is the same as one considered in the recent work by Coladangelo, Vidick, and Zhang (CRYPTO '20). Our construction has the so-called dual-mode property, which means that there are two computationally indistinguishable modes of generating CRS, and we have information theoretical soundness in one mode and information theoretical zero-knowledge property in the other. This answers an open problem left by Coladangelo et al, which is to achieve either of soundness or zero-knowledge information theoretically. To the best of our knowledge, ours is the first dual-mode NIZK for QMA in any kind of model.
We propose three constructions of classically verifiable non-interactive zero-knowledge proofs and arguments (CV-NIZK) for QMA in various preprocessing models. - We construct a CV-NIZK for QMA in the quantum secret parameter model where a trusted setup sends a quantum proving key to the prover and a classical verification key to the verifier. It is information theoretically sound and zero-knowledge. - Assuming the quantum hardness of the learning with errors problem, we construct a CV-NIZK for QMA in a model where a trusted party generates a CRS and the verifier sends an instance-independent quantum message to the prover as preprocessing. This model is the same as one considered in the recent work by Coladangelo, Vidick, and Zhang (CRYPTO '20). Our construction has the so-called dual-mode property, which means that there are two computationally indistinguishable modes of generating CRS, and we have information theoretical soundness in one mode and information theoretical zero-knowledge property in the other. This answers an open problem left by Coladangelo et al, which is to achieve either of soundness or zero-knowledge information theoretically. To the best of our knowledge, ours is the first dual-mode NIZK for QMA in any kind of model. - We construct a CV-NIZK for QMA with quantum preprocessing in the quantum random oracle model. This quantum preprocessing is the one where the verifier sends a random Pauli-basis states to the prover. Our construction uses the Fiat-Shamir transformation. The quantum preprocessing can be replaced with the setup that distributes Bell pairs among the prover and the verifier, and therefore we solve the open problem by Broadbent and Grilo (FOCS '20) about the possibility of NIZK for QMA in the shared Bell pair model via the Fiat-Shamir transformation.
Homomorphic Encryption and Zero Knowledge Proofs are two trending concepts that are widely popular as data privacy preservation techniques in a wide variety of applications, especially in those associated with the newly evolved block chain technology which are immutable, distributed and secure. Zero knowledge proof is a cryptographic technique can provide proof that a certain statement is correct, without revealing any details about the statement, while homomorphic encryption allows to perform computations on encrypted data without decrypting it. This article explores the significance of the data privacy aspect provided by both ZKP and Homomorphic Encryption and how it can be effectively used to improvise the privacy of blockchain applications in various domains.\n\n
Ayman Alkhalifah, Alex Ng, Paul Watters, A. S. M. Kayes
In Ethereum blockchain, smart contracts are immutable, public, and distributed. However, they are subject to many vulnerabilities stemming from coding errors made by developers. Seven cybersecurity incidents occurred in Ethereum smart contracts between 2016 and 2018, which led to financial losses estimated to be over US$ 289 million. Reentrancy vulnerability was the cause of two of these incidents, and the impacts went far beyond financial loss. Several reentrancy countermeasures are available, which are based on predefined patterns that are used to prevent vulnerability exploitation before the deployment of a smart contract; however, several limitations have been identified in these countermeasures. Motivated by all these issues, the objective of this article is to help developers improve the cybersecurity of smart contracts by proposing a solution that calculates the difference between the contract balance and the total balance of all participants in a smart contract before and after any operation in a transaction that changes its state. Proof-of-concept implementations show that this solution can provide a detection and prevention mechanism against reentrancy attacks during the execution of any smart contract.
Popular blockchains such as Ethereum and several others execute complex transactions in blocks through user-defined scripts known as smart contracts. Serial execution of smart contract transactions/atomic-units (AUs) fails to harness the multiprocessing power offered by the prevalence of multi-core processors. By adding concurrency to the execution of AUs, we can achieve better efficiency and higher throughput. In this paper, we develop a concurrent miner that proposes a block by executing the AUs concurrently using optimistic Software Transactional Memory systems (STMs). It captures the independent AUs in a concurrent bin and dependent AUs in the block graph (BG) efficiently. Later, we propose a concurrent validator that re-executes the same AUs concurrently and deterministically using a concurrent bin followed by a BG given by the miner to verify the proposed block. We rigorously prove the correctness of concurrent execution of AUs and achieve significant performance gain over the state-of-the-art.
Satyabrata Aich, Nday Kabulo Sinai, Saurabh Kumar, Mohammed Al Ali · 7 authors
For decades artificial intelligence (AI) has been used for various applications in the healthcare industry. Machine learning and artificial intelligence algorithms allow us to diagnose and customize medical care and follow-up plans to get better results, and during the covid19 pandemic, it was found that AI models have been using to predict the Covid-19 symptoms, understanding how it spreads, speeding up research and treatment using medical data. However, it is very challenging to make a robust AI model and use it in a real-time and real-world environment since most organizations do not want to share their data with other third parties due to privacy concerns, furthermore, it is difficult to build a generalized prediction model because of the fragmented nature of the patient data across the healthcare system. To solve the above problems, this paper presents a solution based on blockchain and AI technologies. The blockchain will securely protect the data access and AI-based federated learning for building a robust model for global and real-time usage.
Open access
2 source records
Privacy-Preserving Technologies in Data
Blockchain Technology Applications and Security
Artificial Intelligence in Healthcare and Education
Mallikarjun Reddy Dorsala, V. N. Sastry, Chapram Sudhakar
With the advent of mobile crowdsensing, the mobile devices equipped with a variety of sensors (such as accelerometer, gyroscope, microphone etc.) are used to collect sensory data. A data aggregator processes the collected sensor data to deliver various services such as traffic management, health care and environmental monitoring. To ensure the privacy of the data, privacy-preserving aggregation (PPA) has attracted much attention since it can find aggregated statistics on the encrypted data. In this paper, we extend the existing PPA schemes in two directions: (1) Aggregator unforgeability – The aggregator performs the aggregation operation correctly. Although there are some schemes which consider aggregator unforgeability, they rely on cryptographic techniques. (2) Fair payments – The data owners receive the payments for their data contribution if and only if the aggregator receives the data. Contrary to existing works, we achieve the aggregator unforgeability and fair payments by modeling the aggregator as a smart contract running on a public Blockchain network. We design two PPA schemes FairNaivePPA and FairPPA for secure aggregation of MCS data with fair payments. We show the financial and transactional cost analysis of proposed contracts by implementing them in solidity and running them on Ethereum Blockchain.
As the use of internet of things (IoT) devices increases, the importance of security has increased, because personal and private data such as biometrics, images, photos, and voices can be collected. However, there is a possibility of data leakage or manipulation by monopolizing the authority of the data, since such data are stored in a central server by the centralized structure of IoT devices. Furthermore, such a structure has a potential security problem, caused by an attack on the server due to single point vulnerability. Blockchain’s, through their decentralized structure, effectively solve the single point vulnerability, and their consensus algorithm allows network participants to verify data without any monopolizing. Therefore, blockchain technology becomes an effective solution for solving the security problem of the IoT’s centralized method. However, current blockchain technology is not suitable for IoT devices. Blockchain technology requires large storage space for the endless append-only block storing, and high CPU processing power for performing consensus algorithms, while its opened block access policy exposes private data to the public. In this paper, we propose a decentralized lightweight blockchain, named Fusion Chain, to support IoT devices. First, it solves the storage size issue of the blockchain by using the interplanetary file system (IPFS). Second, it does not require high computational power by using the practical Byzantine fault tolerance (PBFT) consensus algorithm. Third, data privacy is ensured by allowing only authorized users to access data through public key encryption using PKI. Fusion Chain was implemented from scratch written using Node.js and golang. The results show that the proposed Fusion Chain is suitable for IoT devices. According to our experiments, the size of the blockchain dramatically decreased, and only 6% of CPU on an ARM core, and 49 MB of memory, is used on average for the consensus process. It also effectively protects privacy data by using a public key infrastructure (PKI).
Due to the need to maintain confidentiality, redundancy, and openness, the usage of Blockchain and other DLTs has dramatically advanced in recent years, and is being recommended for various applications. In blockchain, these capabilities are supplied by means of hash functions and public-key encryption. However, the rapid development of quantum computation in the near future has opened the door to the Grover and Shor algorithms. These algorithms challenge both public and hash encryption, causing blockchains to redesign and use quantum attack-tolerant cryptosystems; this produces cryptosystems which are considered post-quantum cryptosystems, which are quantum-resistant. This paper reviews current scientists on quantum blockchain for such purposes. In addition, the major challenges are studied with the most important post-quantum blockchain systems. In addition, the most promising post quantum signature encryption and digital blockchain signature schemes are detailed in terms of the functionality and durability of the most promising public signatures. In this article, researchers and developers in blockchain have an extensive perspective and practical advice on post-quantum blockchain protection.
Increasingly, information systems rely on computational, storage, and network resources deployed in third-party facilities such as cloud centers and edge nodes. Such an approach further exacerbates cybersecurity concerns constantly raised by numerous incidents of security and privacy attacks resulting in data leakage and identity theft, among others. These have, in turn, forced the creation of stricter security and privacy-related regulations and have eroded the trust in cyberspace. In particular, security-related services and infrastructures, such as Certificate Authorities (CAs) that provide digital certificate services and Third-Party Authorities (TPAs) that provide cryptographic key services, are critical components for establishing trust in crypto-based privacy-preserving applications and services. To address such trust issues, various transparency frameworks and approaches have been recently proposed in the literature. This paper proposes TAB framework that provides transparency and trustworthiness of third-party authority and third-party facilities using blockchain techniques for emerging crypto-based privacy-preserving applications. TAB employs the Ethereum blockchain as the underlying public ledger and also includes a novel smart contract to automate accountability with an incentive mechanism that motivates users to participate in auditing, and punishes unintentional or malicious behaviors. We implement TAB and show through experimental evaluation in the Ethereum official test network, Rinkeby, that the framework is efficient. We also formally show the security guarantee provided by TAB, and analyze the privacy guarantee and trustworthiness it provides.
The Hashgraph consensus algorithm is an algorithm for asynchronous Byzantine fault tolerance intended for distributed shared ledgers. Its main distinguishing characteristic is it achieves consensus without exchanging any extra messages; each participant's votes can be determined from public information, so votes need not be transmitted. In this paper, we discuss our experience formalizing the Hashgraph algorithm and its correctness proof using the Rocq proof assistant. The paper is self-contained; it includes a complete discussion of the algorithm and its correctness argument in English.
Abstract The sharing of electronic health records (EHR) has shown significant advantages in the accurate diagnosis of patients and the development of medical institutions. However, due to the privacy and sensitivity of medical data, it is easy to cause security issues such as difficulty in data sharing among different medical institutions and easy leakage of data privacy. Because the blockchain has the characteristics of non-tampering, anonymity, and decentralization. We propose a blockchain-based searchable proxy re-encryption scheme for EHR security storage and sharing. First, we use blockchain and cloud server to store encrypted EHR together to prevent EHR from being tampered with and leaked. Secondly, we use a certificateless encryption and proxy re-encryption based on identity and type scheme as a data sharing protocol. Meanwhile, searchable encryption technology is used to generate a keyword index. Moreover, the proxy node is selected by the delegated proof-of-stake (DPOS) consensus algorithm, which ensures the privacy, immutability and security. It realizes the safe access of third-party data users to medical health data. Finally, security analysis and evaluation show that our scheme can resist identity disguise and replay attacks. In addition, it has stronger security and higher efficiency.
The development of educational informatization makes data privacy particularly important in education. With society's development, the education system is complicated, and the result of education evaluation becomes more and more critical to students. The evaluation process of education must be justice and transparent. In recent years, the Onscreen Marking (OSM) system based on traditional cloud platforms has been widely used in various large-scale public examinations. However, due to the excessive concentration of power in the existing scheme, the mainstream marking process is not transparent, and there are hidden dangers of black-box operation, which will damage the fairness of the examination. In addition, issues related to data security and privacy are still considered to be severe challenges. This paper deals with the above problems by providing secure and private transactions in a distributed OSM assuming the semi-trusted examination center. We have implemented a proof-of-concept for a consortium blockchain-based OSM in a privacy-preserving and auditable manner, enabling markers to mark on the distributed ledger anonymously. We have proposed a distributed OSM system in high-level, which provides theoretical support for the fair evaluation process of education informatization. It has particular theoretical and application value for education combined with blockchain.
Due to the evergrowing blockchain ecosystem, interoperability has become a matter of great importance. Atomic swaps allow connecting otherwise isolated blockchains while adhering to the core principles of censorship resistance and permissionlessnes. Up until recently, atomic swap protocols have mostly relied on complex script support, excluding certain types of blockchains. With advances in cryptography, it is now possible to build a bridge between almost any two blockchains. In this work, we give an explanation of one such protocol which applies adaptor signatures on Bitcoin to procure atomic swaps between Monero and Bitcoin. We dive into the cryptographic details, discuss its limitations and give an outlook on our current work where we use adaptor signatures on the Monero signature scheme.
Federated Learning is a promising machine learning paradigm when multiple parties collaborate to build a high-quality machine learning model. Nonetheless, these parties are only willing to participate when given enough incentives, such as a fair reward based on their contributions. Many studies explored Shapley value based methods to evaluate each party's contribution to the learned model. However, they commonly assume a semi-trusted server to train the model and evaluate the data owners' model contributions, which lacks transparency and may hinder the success of federated learning in practice. In this work, we propose a blockchain-based federated learning framework and a protocol to transparently evaluate each participant's contribution. Our framework protects all parties' privacy in the model building phase and transparently evaluates contributions based on the model updates. The experiment with the handwritten digits dataset demonstrates that the proposed method can effectively evaluate the contributions.
Reputation expresses the beliefs or opinions about someone or something that are held by an individual or by a community. Reputation Management Systems (RMSs) handle representation, computation, and storage of reputation in some quantitative form, suitable for grounding trust relations among parties. Quantifying reputation is important in situations, like online service provision, which involve interaction between parties who do not know (and potentially distrust) each other. The basic idea is to let parties rate each other. When a party is considered for interaction, its ratings can be aggregated in order to derive a score for deciding whether to trust it or not. While much valuable research work has been done on reputation-based trust schemes, the problem of establishing collective trust in the reputation management system itself has never been fully solved. Recently, several researchers have put forward the idea of using Distributed Ledger Technology (DLT) as the foundation for implementing trustworthy RMSs. The purpose of this paper is to identify some critical problems that arise when DLTs are used in order to manage evidence about previous interaction and compute reputations. The paper proposes some practical solutions and describes methods to deploy them on top of standard DLT of the Ethereum family.
The internet of things (IoT) devices are expected to collect vast amounts of data that support different kinds of applications such as health monitor, smart home, and traffic management. However, its characteristics such as resource-constrained nature, dynamicity, and large-scale growth bring challenges to secure IoT data sharing. Nowadays, blockchain-based ciphertext-policy attribute-based encryption (CP-ABE) was proposed to realize secure IoT data sharing. In blockchain-based CP-ABE data sharing schemes, the data are encrypted and stored in the cloud. Once users want to process the data, they should download and then decrypt the ciphertext in the client-end, and after processing the data, users encrypt and upload the ciphertext onto the cloud. This outweighs the advantage of using cloud computing resources. Fully homomorphic encryption (FHE) and homomorphic signature technology may be adopted to realize ciphertext computation and for correctness checking of ciphertext computation results. In this paper, we propose a secure and computable IoT data sharing system to ensure users enjoying the computation convenience of the cloud-end. Specifically, the proposed system integrates CP-ABE and FHE to realize secure IoT data sharing and ciphertext computation. In addition, we generated homomorphic signatures of ciphertexts to enable users to check the correctness of the ciphertext computation results. Moreover, to supervise the cloud, providing the honest IoT data access control, storage, and computing services for users, we recorded the access policy of the data, the hash of the data, the signature of the ciphertext, and the homomorphic signature of the ciphertext on the blockchain. The performance evaluation and security analysis show the proposed scheme is practical and secure.
The emergence of the cloud storage has brought great convenience to people’s life. Many individuals and enterprises have delivered a large amount of data to the third-party server for storage. Thus, the privacy protection of data retrieved by the user needs to be guaranteed. Searchable encryption technology for the cloud environment is adopted to ensure that the user information is secure with retrieving data. However, most schemes only support single-keyword search and do not support file updates, which limit the flexibility of the scheme. To eliminate these problems, we propose a blockchain-enabled public key encryption scheme with multi-keyword search (BPKEMS), and our scheme supports file updates. In addition, smart contract is used to ensure the fairness of transactions between data owner and user without introducing a third party. At the data storage stage, our scheme realizes the verifiability by numbering the files, which ensures that the ciphertext received by the user is complete. In terms of security and performance, our scheme is secure against inside keyword guessing attacks (KGAs) and has better computation overhead than other related schemes.
Jun Li, Yumeng Shao, Kang Wei, Ming Ding · 8 authors
Federated learning (FL), as a distributed machine learning paradigm, promotes personal privacy by local data processing at each client. However, relying on a centralized server for model aggregation, standard FL is vulnerable to server malfunctions, untrustworthy servers, and external attacks. To address these issues, we propose a decentralized FL framework by integrating blockchain into FL, namely, blockchain assisted decentralized federated learning (BLADE-FL). In a round of the proposed BLADE-FL, each client broadcasts its trained model to other clients, aggregates its own model with received ones, and then competes to generate a block before its local training on the next round. We evaluate the learning performance of BLADE-FL, and develop an upper bound on the global loss function. Then we verify that this bound is convex with respect to the number of overall aggregation rounds$K$, and optimize the computing resource allocation for minimizing the upper bound. We also note that there is a critical problem of training deficiency, caused by lazy clients who plagiarize others’ trained models and add artificial noises to disguise their cheating behaviors. Focusing on this problem, we explore the impact of lazy clients on the learning performance of BLADE-FL, and characterize the relationship among the optimal$K$, the learning parameters, and the proportion of lazy clients. Based on the MNIST and Fashion-MNIST datasets, we see that the experimental results are consistent with the analytical ones. To be specific, the gap between the developed upper bound and experimental results is lower than$5\%$, and the optimized$K$based on the upper bound can effectively minimize the loss function.
Orestis Alpos, Christian Cachin, Giorgia Azzurra Marson, Luca Zanolini
Modern blockchains support a variety of distributed applications beyond cryptocurrencies, including smart contracts -- which let users execute arbitrary code in a distributed and decentralized fashion. Regardless of their intended application, blockchain platforms implicitly assume consensus for the correct execution of a smart contract, thus requiring that all transactions are totally ordered. It was only recently recognized that consensus is not necessary to prevent double-spending in a cryptocurrency (Guerraoui et al., PODC'19), contrary to common belief. This result suggests that current implementations may be sacrificing efficiency and scalability because they synchronize transactions much more tightly than actually needed. In this work, we study the synchronization requirements of Ethereum's ERC20 token contract, one of the most widely adopted smart contacts. Namely, we model a smart-contract token as a concurrent object and analyze its consensus number as a measure of synchronization power. We show that the richer set of methods supported by ERC20 tokens, compared to standard cryptocurrencies, results in strictly stronger synchronization requirements. More surprisingly, the synchronization power of ERC20 tokens depends on the object's state and can thus be modified by method invocations. To prove this result, we develop a dedicated framework to express how the object's state affects the needed synchronization level. Our findings indicate that ERC20 tokens, as well as other token standards, are more powerful and versatile than plain cryptocurrencies, and are subject to dynamic requirements. Developing specific synchronization protocols that exploit these dynamic requirements will pave the way towards more robust and scalable blockchain platforms.
This paper presents EDSC, a novel smart contract platform design based on the event-driven execution model as opposed to the traditionally employed transaction-driven execution model. We reason that such a design is a better fit for many emerging smart contract applications and is better positioned to address the scalability and performance challenges plaguing the smart contract ecosystem. We propose EDSC's design under the Ethereum framework, and the design can be easily adapted for other existing smart contract platforms. We have conducted implementation using Ethereum client and experiments where performance modeling results show on average 2.2 to 4.6 times reduced total latency of event triggered smart contracts, which demonstrates its effectiveness for supporting contracts that demand timely execution based on events. In addition, we discuss example use cases to demonstrate the design's utility and comment on its potential security dynamics.
Permissionless blockchains such as Bitcoin have excelled at financial services. Yet, opportunistic traders extract monetary value from the mesh of decentralized finance (DeFi) smart contracts through so-called blockchain extractable value (BEV). The recent emergence of centralized BEV relayer portrays BEV as a positive additional revenue source. Because BEV was quantitatively shown to deteriorate the blockchain's consensus security, BEV relayers endanger the ledger security by incentivizing rational miners to fork the chain. For example, a rational miner with a 10% hashrate will fork Ethereum if a BEV opportunity exceeds 4x the block reward. However, related work is currently missing quantitative insights on past BEV extraction to assess the practical risks of BEV objectively. In this work, we allow to quantify the BEV danger by deriving the USD extracted from sandwich attacks, liquidations, and decentralized exchange arbitrage. We estimate that over 32 months, BEV yielded 540.54M USD in profit, divided among 11,289 addresses when capturing 49,691 cryptocurrencies and 60,830 on-chain markets. The highest BEV instance we find amounts to 4.1M USD, 616.6x the Ethereum block reward. Moreover, while the practitioner's community has discussed the existence of generalized trading bots, we are, to our knowledge, the first to provide a concrete algorithm. Our algorithm can replace unconfirmed transactions without the need to understand the victim transactions' underlying logic, which we estimate to have yielded a profit of 57,037.32 ETH (35.37M USD) over 32 months of past blockchain data. Finally, we formalize and analyze emerging BEV relay systems, where miners accept BEV transactions from a centralized relay server instead of the peer-to-peer (P2P) network. We find that such relay systems aggravate the consensus layer attacks and therefore further endanger blockchain security.
Hang Chen, Syed Ali Asif, Jihong Park, Chien-Chung Shen · 5 authors
Federated learning (FL) is a promising distributed learning solution that only exchanges model parameters without revealing raw data. However, the centralized architecture of FL is vulnerable to the single point of failure. In addition, FL does not examine the legitimacy of local models, so even a small fraction of malicious devices can disrupt global training. To resolve these robustness issues of FL, in this paper, we propose a blockchain-based decentralized FL framework, termed VBFL, by exploiting two mechanisms in a blockchained architecture. First, we introduced a novel decentralized validation mechanism such that the legitimacy of local model updates is examined by individual validators. Second, we designed a dedicated proof-of-stake consensus mechanism where stake is more frequently rewarded to honest devices, which protects the legitimate local model updates by increasing their chances of dictating the blocks appended to the blockchain. Together, these solutions promote more federation within legitimate devices, enabling robust FL. Our emulation results of the MNIST classification corroborate that with 15% of malicious devices, VBFL achieves 87% accuracy, which is 7.4x higher than Vanilla FL.