Blockchains combine other technologies, such as cryptography, networking, and incentive mechanisms, to enable the creation, validation, and recording of transactions between participating nodes. A consensus algorithm is used in a blockchain system to determine the shared state among distributed nodes. An important component underlying any blockchain-based system is its consensus mechanism, which principally determines the performance and security of the overall system. As the nature of peer-to- peer(P2P) networks is open and dynamic, the security risk within that environment is greatly increased mostly because nodes can join and leave the network at will. Thus, it is important to have a system that can check against malicious behaviour. In this work, we propose a reputation-based consensus mechanism for blockchain-based systems, Proof-of-Reputation(PoR) where the nodes with the highest reputation values eventually become part of a consensus group that determines the state of the blockchain.
Deep learning has achieved the high-accuracy of state-of-the-art algorithms in long-standing AI tasks. Due to the obvious privacy issues of deep learning, Google proposes Federal Deep Learning (FDL), in which distributed participants only upload local gradients and and a centralized server updates parameters based on the collected gradients. But few users are willing to participate in federated learning due to the lack of contribution evaluation and reward mechanisms. So a decentralized federated deep learning, called DFDL, has been proposed by introducing blockchain to form an effective incentive mechanism for participants. However, DFDL still faces serious privacy issues as blockchain does not guarantee the privacy of training data and model. In this paper, in order to address the aforementioned issues, we propose a new Privacy-preserving DFDL scheme, called PDFDL. With PDFDL, parties can securely learn a global model with their local gradients in the assistance of blockchain, and the parties’ sensitive data and the global model are well protected. Specifically, with a secure multi-party aggregation computing, all local gradients are encrypted by their owners before being sent to the smart contract, and can be directly aggregated without decryption. Detailed security analysis shows that PDFDL can resist various known security threats. Moreover, we give an implementation prototype by integrating deep learning module with a Blockchain development platform (Ethereum V1.6.4). We demonstrate the encryption performance and the training accuracy of our PDFDL on benchmark datasets.
A wider coverage and a better solution to latency reduction in 5G necessitates its combination with mobile edge computing (MEC) technology. Decentralized deep learning (DDL) as a promising solution to privacy-preserving data processing for millions of edge smart devices, it leverages federated learning within the networking of local models, without disclosing a client's raw data. Especially, in industries such as finance and healthcare where sensitive data of transactions and personal medical records is cautiously maintained, DDL facilitates the collaboration among these institutes to improve the performance of local models, while protecting data privacy of participating clients. In this survey paper, we demonstrate technical fundamentals of DDL for benefiting many walks of society through decentralized learning. Furthermore, we offer a comprehensive overview of recent challenges of DDL and the most relevant solutions from novel perspectives of communication efficiency and trustworthiness.
We discuss the problem of facilitating tax auditing assuming "programmable money", i.e., digital monetary instruments that are managed by an underlying distributed ledger. We explore how a taxation authority can verify the declared returns of its citizens and create a counter-incentive to tax evasion by two distinct mechanisms. First, we describe a design which enables auditing it as a built-in feature with minimal changes on the underlying ledger's consensus protocol. Second, we offer an application-layer extension, which requires no modification in the underlying ledger's design. Both solutions provide a high level of privacy, ensuring that, apart from specific limited data given to the taxation authority, no additional information - beyond the information already published on the underlying ledger - is leaked.
Angtai Li, Guohua Tian, Meixia Miao, Jianpeng Gong
In cloud storage, public auditing is a more popular data integrity verification technique since it allows users to delegate auditing tasks to a fully trusted third-party auditor (TPA). However, it is difficult to find such a TPA in practical application. Besides, the centralised auditing model makes TPA have to bear burdensome work pressure, which limits the practicability of existing schemes. In this paper, we firstly proposed a blockchain-based generalised shared auditing mechanism BCSA in the cross-user scenario, which aims at achieving available public auditing with a non-fully trusted TPA, and reducing the user's auditing fees and TPA's work pressure by allowing data users to share their auditing procedure with others. Furthermore, we initialise a concrete construction BCSAD with Diffie–Hellman protocol for the cross-user auditing scenario with different data. Likewise, we also propose a novel construction BCSAI for the cross-user auditing scenario with identical data, which utilises a password-authenticated key exchange (PAKE) protocol to achieve shared auditing and ciphertext deduplication, reducing data storage and auditing fees for data users and alleviating service pressure on the cloud server and TPA. Security and performance analysis evaluate the practicability of the proposed scheme.
The rise of Ethereum and other blockchains that support smart contracts has\nled to the creation of decentralized exchanges (DEXs), such as Uniswap,\nBalancer, Curve, mStable, and SushiSwap, which enable agents to trade\ncryptocurrencies without trusting a centralized authority. While traditional\nexchanges use order books to match and execute trades, DEXs are typically\norganized as constant function market makers (CFMMs). CFMMs accept and reject\nproposed trades based on the evaluation of a function that depends on the\nproposed trade and the current reserves of the DEX. For trades that involve\nonly two assets, CFMMs are easy to understand, via two functions that give the\nquantity of one asset that must be tendered to receive a given quantity of the\nother, and vice versa. When more than two assets are being exchanged, it is\nharder to understand the landscape of possible trades. We observe that various\nproblems of choosing a multi-asset trade can be formulated as convex\noptimization problems, and can therefore be reliably and efficiently solved.\n
Abstract Existing models for non-interactive MPC cannot provide full privacy for inputs, because they inherently leak the residual function (i.e., the output of the function on the honest parties’ input together with all possible values of the adversarial inputs). For example, in any non-interactive sealed-bid auction, the last bidder can figure out what was the highest previous bid. We present a new MPC model which avoids this privacy leak. To achieve this, we utilize a blockchain in a novel way, incorporating smart contracts and arbitrary parties that can be incentivized to perform computation (“bounty hunters,” akin to miners). Security is maintained under a monetary assumption about the parties: an honest party can temporarily supply a recoverable collateral of value higher than the computational cost an adversary can expend. We thus construct non-interactive MPC protocols with strong security guarantees (full security, no residual leakage) in the short term. Over time, as the adversary can invest more and more computational resources, the security guarantee decays. Thus, our model, which we call Gage MPC, is suitable for secure computation with limited-time secrecy, such as auctions. A key ingredient in our protocols is a primitive we call “Gage Time Capsules” (GaTC): a time capsule that allows a party to commit to a value that others are able to reveal but only at a designated computational cost. A GaTC allows a party to commit to a value together with a monetary collateral. If the original party properly opens the GaTC, it can recover the collateral. Otherwise, the collateral is used to incentivize bounty hunters to open the GaTC. This primitive is used to ensure completion of Gage MPC protocols on the desired inputs. As a requisite tool (of independent interest), we present a generalization of garbled circuit that are more robust: they can tolerate exposure of extra input labels. This is in contrast to Yao’s garbled circuits, whose secrecy breaks down if even a single extra label is exposed. Finally, we present a proof-of-concept implementation of a special case of our construction, yielding an auction functionality over an Ethereum-like blockchain.
Abstract This paper presents HashWires, a hash-based range proof protocol that is applicable in settings for which there is a trusted third party (typically a credential issuer) that can generate commitments. We refer to these as “credential-based” range proofs (CBRPs). HashWires improves upon hashchain solutions that are typically restricted to micro-payments for small interval ranges, achieving an exponential speedup in proof generation and verification time. Under reasonable assumptions and performance considerations, a Hash-Wires proof can be as small as 305 bytes for 64-bit integers. Although CBRPs are not zero-knowledge and are inherently less flexible than general zero-knowledge range proofs, we provide a number of applications in which a credential issuer can leverage HashWires to provide range proofs for private values, without having to rely on heavyweight cryptographic tools and assumptions.
Abstract The General Data Protection Regulation highlights the principle of data minimization, which means that only data required to successfully accomplish a given task should be processed. In this paper, we propose a Blockchain-based scheme that allows users to have control over the personal data revealed when accessing a service. The proposed solution does not rely on sophisticated cryptographic primitives, provides mechanisms for revoking the authorization to access a service and for guessing the identity of a user only in cases of need, and is compliant with the recent eIDAS Regulation. We prove that the proposed scheme is secure and reaches the expected goal, and we present an Ethereum-based implementation to show the effectiveness of the proposed solution.
Monik Raj Behera, Sudhir K. Upadhyay, Suresh Shetty
Over the recent years, Federated machine learning continues to gain interest\nand momentum where there is a need to draw insights from data while preserving\nthe data provider's privacy. However, one among other existing challenges in\nthe adoption of federated learning has been the lack of fair, transparent and\nuniversally agreed incentivization schemes for rewarding the federated learning\ncontributors. Smart contracts on a blockchain network provide transparent,\nimmutable and independently verifiable proofs by all participants of the\nnetwork. We leverage this open and transparent nature of smart contracts on a\nblockchain to define incentivization rules for the contributors, which is based\non a novel scalar quantity - federated contribution. Such a smart contract\nbased reward-driven model has the potential to revolutionize the federated\nlearning adoption in enterprises. Our contribution is two-fold: first is to\nshow how smart contract based blockchain can be a very natural communication\nchannel for federated learning. Second, leveraging this infrastructure, we can\nshow how an intuitive measure of each agents' contribution can be built and\nintegrated with the life cycle of the training and reward process.\n
Self-sovereign identity is the next evolution of identity management models. This survey takes a journey through the origin of identity, defining digital identity and progressive iterations of digital identity models leading up to self-sovereign identity. It then states the relevant research initiatives, platforms, projects, and regulatory frameworks, as well as the building blocks including decentralized identifiers, verifiable credentials, distributed ledger, and various privacy engineering protocols. Finally, the survey provides an overview of the key challenges and research opportunities around self-sovereign identity.
Federated learning (FL) has experienced a boom in recent years, which is jointly promoted by the prosperity of machine learning and Artificial Intelligence along with emerging privacy issues. In the FL paradigm, a central server and local end devices maintain the same model by exchanging model updates instead of raw data, with which the privacy of data stored on end devices is not directly revealed. In this way, the privacy violation caused by the growing collection of sensitive data can be mitigated. However, the performance of FL with a central server is reaching a bottleneck, while new threats are emerging simultaneously. There are various reasons, among which the most significant ones are centralized processing, data falsification, and lack of incentives. To accelerate the proliferation of FL, blockchain-enabled FL has attracted substantial attention from both academia and industry. A considerable number of novel solutions are devised to meet the emerging demands of diverse scenarios. Blockchain-enabled FL provides both theories and techniques to improve the performance of FL from various perspectives. In this survey, we will comprehensively summarize and evaluate existing variants of blockchain-enabled FL, identify the emerging challenges, and propose potentially promising research directions in this under-explored domain.
We describe a fast three-round mutual authentication protocol for parties A and B belonging to the same coalition group. Parties A and B keep their own independent long-term private keys that are used in the process of authentication and can be used for other purposes. The scheme assumes an initial setup with a trusted third party T. This party initiates another secret information that includes factors of a large RSA modulus. For authentication, both parties must demonstrate each other the knowledge of their private keys without revealing them and the ability to factorize a large RSA modulus. Thus, the protocol based on the suggested scheme provides reciprocal authentication. The scheme possesses all desirable properties of an interactive proof, i.e., completeness, soundness, and zero-knowledge. The security of the protocol relies on assumptions of difficulty of the RSA factorization and existence of a cryptographic hash function.
Ulrich Haböck, Alberto Garoffolo, Daniele Di Benedetto
This document describes Darlin, a succinct zero-knowledge argument of knowledge based on the Marlin SNARK (Chiesa et al., Eurocrypt 2020) and the `dlog' polynomial commitment scheme from Bootle et al. EUROCRYPT 2016. Darlin addresses recursive proofs by integrating the amortization technique from Halo (IACR eprint 2019/099) for the non-succinct parts of the dlog verifier, and we adapt their strategy for bivariate circuit encoding polynomials to aggregate Marlin's inner sumchecks across the nodes the recursive scheme. We estimate the performance impact of inner sumcheck aggregation by about 30% in a tree-like scheme of in-degree 2, and beyond when applied to linear recursion.
Proof-of-Stake blockchains based on a longest-chain consensus protocol are an attractive energy-friendly alternative to the Proof-of-Work paradigm. However, formal barriers to "getting the incentives right" were recently discovered, driven by the desire to use the blockchain itself as a source of pseudorandomness \cite{brown2019formal}. We consider instead a longest-chain Proof-of-Stake protocol with perfect, trusted, external randomness (e.g. a randomness beacon). We produce two main results. First, we show that a strategic miner can strictly outperform an honest miner with just $32.5\%$ of the total stake. Note that a miner of this size {\em cannot} outperform an honest miner in the Proof-of-Work model. This establishes that even with access to a perfect randomness beacon, incentives in Proof-of-Work and Proof-of-Stake longest-chain protocols are fundamentally different. Second, we prove that a strategic miner cannot outperform an honest miner with $30.8\%$ of the total stake. This means that, while not quite as secure as the Proof-of-Work regime, desirable incentive properties of Proof-of-Work longest-chain protocols can be approximately recovered via Proof-of-Stake with a perfect randomness beacon. The space of possible strategies in a Proof-of-Stake mining game is {\em significantly} richer than in a Proof-of-Work game. Our main technical contribution is a characterization of potentially optimal strategies for a strategic miner, and in particular, a proof that the corresponding infinite-state MDP admits an optimal strategy that is positive recurrent.
Lukas Burkhalter, Hidde Lycklama, Alexander Viand, Nicolas Küchler · 5 authors
Federated Learning is an emerging decentralized machine learning paradigm that allows a large number of clients to train a joint model without the need to share their private data. Participants instead only share ephemeral updates necessary to train the model. To ensure the confidentiality of the client updates, Federated Learning systems employ secure aggregation; clients encrypt their gradient updates, and only the aggregated model is revealed to the server. Achieving this level of data protection, however, presents new challenges to the robustness of Federated Learning, i.e., the ability to tolerate failures and attacks. Unfortunately, in this setting, a malicious client can now easily exert influence on the model behavior without being detected. As Federated Learning is being deployed in practice in a range of sensitive applications, its robustness is growing in importance. In this paper, we take a step towards understanding and improving the robustness of secure Federated Learning. We start this paper with a systematic study that evaluates and analyzes existing attack vectors and discusses potential defenses and assesses their effectiveness. We then present RoFL, a secure Federated Learning system that improves robustness against malicious clients through input checks on the encrypted model updates. RoFL extends Federated Learning's secure aggregation protocol to allow expressing a variety of properties and constraints on model updates using zero-knowledge proofs. To enable RoFL to scale to typical Federated Learning settings, we introduce several ML and cryptographic optimizations specific to Federated Learning. We implement and evaluate a prototype of RoFL and show that realistic ML models can be trained in a reasonable time while improving robustness.
This perspectives article surveys the most promising privacy-preserving cryptographic technologies including secure multiparty computation, zero-knowledge proofs and fully homomorphic encryption, and their various real-world applications.
Oliver Stengele, Markus Raiber, Jörn Müller‐Quade, Hannes Hartenstein
We address the Threshold Information Disclosure (TID) problem on Ethereum: An arbitrary number of users commit to the scheduled disclosure of their individual messages recorded on the Ethereum blockchain if and only if all such messages are disclosed. Before a disclosure, only the original sender of each message should know its contents. To accomplish this, we task a small council with executing a distributed generation and threshold sharing of an asymmetric key pair. The public key can be used to encrypt messages which only become readable once the threshold-shared decryption key is reconstructed at a predefined point in time and recorded on-chain. With blockchains like Ethereum, it is possible to coordinate such procedures and attach economic stakes to the actions of participating individuals. In this paper, we present ETHTID, an Ethereum smart contract application to coordinate Threshold Information Disclosure. We base our implementation on ETHDKG [1], a smart contract application for distributed key generation and threshold sharing, and adapt it to fit our differing use case as well as add functionality to oversee a scheduled reconstruction of the decryption key. For our main cost saving optimisation, we show that the security of the underlying cryptographic scheme is maintained. We evaluate how the execution costs depend on the size of the council and the threshold and show that the presented protocol is deployable on Ethereum with a council of more than 200 members with gas savings of 20-40% compared to ETHDKG.
Shichang Xuan, Ming Jin, Xin Li, Zhaoyuan Yao · 6 authors
The rapid development in network technology has resulted in the proliferation of Internet of Things (IoT). This trend has led to a widespread utilization of decentralized data and distributed computing power. While machine learning can benefit from the massive amount of IoT data, privacy concerns and communication costs have caused data silos. Although the adoption of blockchain and federated learning technologies addresses the security issues related to collusion attacks and privacy leakage in data sharing, the “free-rider attacks” and “model poisoning attacks” in the federated learning process require auditing of the training models one by one. However, that increases the communication cost of the entire training process. Hence, to address the problem of increased communication cost due to node security verification in the blockchain-based federated learning process, we propose a communication cost optimization method based on security evaluation. By studying the verification mechanism for useless or malicious nodes, we also introduce a double-layer aggregation model into the federated learning process by combining the competing voting verification methods and aggregation algorithms. The experimental comparisons verify that the proposed model effectively reduces the communication cost of the node security verification in the blockchain-based federated learning process.
P Bhanupriya, Sabitha Gauni, K Kalimuthu, C. T. Manimegalai
Abstract Blockchain has recently become an essential tool which enables sensitive cloud services without the need for central confidence. For example, several different cryptocurrencies were permitted with public blockchains. Unfortunately, confidential details may be exposed on current public blockchain and smart contracts implemented there. Whilst some continuous work is under way to resolve these insecure knowledge leakage problems using advanced cryptography, they need major improvements on current and common Blockchain technology such as Ethereum and are typically costly in computing. On the other hand, blockchain applications were proposed to allow the data exchange among the pre accepted nodes/participants to be more efficient and privacy-preserving. While private blockchains respond to certain challenges of privacy by allowing only the particular community of participants to view sensitive data, they do not allow public transparency for communications because businesses are accepted by a known number of users also cannot be freely viewed. One natural problem is whether we should use public and private Blockchain networks in order to allow effective, improve privacy and accountable applications in view of these findings? In this work, we try in connection with digital auctions to face this challenge. In specific, we provide a newly designed blockchain architecture combined with private and open blockchains which enables sensitive offers to be opened up on a secluded blockchain so solitary the merchant can study the offers, and none of others. We also use shared blockchains to report the public sale winner and to make transfers responsible. Moreover, we demonstrate how we can promote sincere activity among auction participants by using intelligent contracts on public blockchains. Our detailed analytical findings suggest that it’s more cost effective compared to pure public auction implementations based on blockchain.
Abstract With the large-scale application of cloud storage systems, lots of attribute-based access control (ABAC) schemes have been introduced to protect data and user security in this insecure environment. ABAC can make data owners control their own data and protect data security and privacy. However, there are two serious privacy leakage problems, namely user attribute privacy and policy privacy, should be solved in the process of constructing ABAC schemes. In this paper, an ABAC scheme supporting privacy protection is constructed, which can solve privacy leakage problems of user attribute privacy and policy privacy in large universe attribute system. Furthermore, a framework is proposed. The framework combines our ABAC scheme, the Ethereum blockchain and blockchain-based storage system. In this framework, the security characteristics of blockchain technology are used to realize decentralization, tamper-resistant and avoiding single point of failure. Besides, the problems of attribute revocation and policy updating are solved by smart contract on the Ethereum blockchain. Finally, we established an initial implementation on Linux and Rinkeby test network, and the experimental results show that our scheme is feasible.
The rise of Internet of Things (IoT) technology promotes the rapid development of location services industry. The idea of smart connectivity also provides a new direction for Location-Based Social Networks (LBSNs). However, due to limited calculate ability and internal storage space of IoT devices, historical location data of users is generally stored in the central server, which is likely to cause the disclosure of users’ private data. In this paper, we propose a Blockchain-enabled Privacy-Preserving Location Sharing (B-PPLS) scheme, which is a new framework that not only protects user location privacy but also provides effective location sharing services for users. For B-PPLS, location data owners can share the location area instead of location coordinates to Requesters, in order to realize the location privacy preserving. Also, the Merkle hash tree is utilized to divide the location area, so as to realize the multilevel privacy preserving. Furthermore, four algorithms are proposed to achieve the four stages of initialization, location record, location sharing, and location verification, respectively. Finally, we analyze the security of the proposed B-PPLS scheme and compare the performance with other related location privacy-preserving schemes by experimental evaluation.
Intuitively there is a drastic distinction between the pure decentralized block-chain systems like Defis and those that only utilize block-chain as an enhancing technology but remain centralized with real-world business model and conventional technologies like database, application server, etc. Our study explores extensively this distinction from a methodological point of view, classifies them into blockchain-complete and blockchain-partial, analyzes key features of the two types, and reveals the root cause of this distinction. We analyze the function or, in more strong words, the "ultimate purpose" of blockchain in the blockchain-partial systems, and present a conceptual model we named proof-chain that quite satisfactorily represented the general paradigm of blockchain in blockchain-partial systems. A universal tension between strength of proof-chain and privacy is then revealed and the zero-knowledge based proof-chain takes shape. Several case studies demonstrate the explaining power of our proof-chain methodology. We then apply proof-chain methodology to the analysis of the ecosystem of a collaborating group of blockchain-partial systems, representing the paradigm of public and private data domain whose border the proof-chain crosses. Finally, some derived guidelines from this methodology speak usefulness of our methodology.