This paper presents a stochastic model for block arrival times based on the difficulty retargeting rule used in Bitcoin, as well as other proof-of-work blockchains. Unlike some previous work, this paper explicitly models the difficulty target as a random variable which is a function of the previous block arrival times and affecting the block times in the next retargeting period. An explicit marginal distribution is derived for the time between successive blocks (the blocktime), while allowing for randomly changing difficulty. This paper also aims to serve as an introduction to Bitcoin and proof-of-work blockchains for the controls community, focusing on the difficulty retargeting procedure used in Bitcoin.
Georgia Avarikioti, Felix Laufenberg, Jakub Sliwinski, Yuyi Wang · 5 authors
Micropayment channels are the most prominent solution to the limitation on transaction throughput in current blockchain systems. However, in practice channels are risky because participants have to be online constantly to avoid fraud, and inefficient because participants have to open multiple channels and lock funds in them. To address the security issue, we propose a novel mechanism that involves watchtowers incentivized to watch the channels and reveal a fraud. Our protocol does not require participants to be online constantly watching the blockchain. The protocol is secure, incentive compatible and lightweight in communication. Furthermore, we present an adaptation of our protocol implementable on the Lightning protocol. Towards efficiency, we examine specific topological structures in the blockchain transaction graph and generalize the construction of channels to enable topologies better suited to specific real-world needs. In these cases, our construction reduces the required amount of signatures for a transaction and the total amount of locked funds in the system.
Blockchain is a decentralized transaction and data management solution, the technological weapon-of-choice behind the success of Bitcoin and other cryptocurrencies. As the number and variety of existing blockchain implementations continues to increase, adopters should focus on selecting the best one to support their decentralized applications (dApps), rather than developing new ones from scratch. In this paper we present a framework to aid software architects, developers, tool selectors and decision makers to adopt the right blockchain technology for their problem at hand. The framework exposes the correlation between technological decisions and architectural features, capturing the knowledge from existing industrial products, technical forums/blogs, experts' feedback and academic literature; plus our own experience using and developing blockchain-based applications. We validate our framework by applying it to dissect the most outstanding blockchain platforms, i.e., the ones behind the top 10 cryptocurrencies apart from Bitcoin. Then, we show how we applied it to a real-world case study in the insurtech domain.
Data is of unprecedented importance today. The most valuable companies of today treat data as a commodity, which they trade and earn revenues. To facilitate such trading, data marketplaces have emerged. Present data marketplaces are inadequate as they fail to satisfy all the desirable properties - fairness, efficiency, security, privacy and adherence to regulations. In this article, we propose a blockchain enabled data marketplace solution that fulfills all required properties. We outline the design, show how to design such a system and discuss the challenges in building a complete data marketplace.
We propose and compare two approaches to identify smart contracts as token systems by analyzing their public bytecode. The first approach symbolically executes the code in order to detect token systems by their characteristic behavior of updating internal accounts. The second approach serves as a comparison base and exploits the common interface of ERC-20, the most popular token standard. We present quantitative results for the Ethereum blockchain, and validate the effectiveness of both approaches using a set of curated token systems as ground truth. We observe 100% recall for the second approach. Recall rates of 89% (with well explainable missed detections) indicate that the first approach may also be able to identify "hidden" or undocumented token systems that intentionally do not implement the standard. One possible application of the proposed methods is to facilitate regulator' tasks of monitoring and policing the use of token systems and their underlying platforms.
Recently, cybersecurity becomes more and more important due to the rapid development of Internet. However, existing methods are in reality highly sensitive to attacks and are far more vulnerable than expected, as they are lack of trustable measures. In this paper, to address the aforementioned problems, we propose a blockchain-based cybersecurity framework, termed as Sapiens Chain, which can protect the privacy of the anonymous users and ensure that the transactions are immutable by providing decentralized and trustable services. Integrating semantic analysis, symbolic execution, and routing learning methods into intelligent auditing, this framework can achieve good accuracy for detecting hidden vulnerabilities. In addition, a revenue incentive mechanism, which aims to donate participants, is built. The practical results demonstrate the effectiveness of the proposed framework.
Yunhui Long, Tanmay Gangwani, Haris Mughees, Carl A. Gunter
Privacy preserving multi-party computation has many applications in areas such as medicine and online advertisements. In this work, we propose a framework for distributed, secure machine learning among untrusted individuals. The framework consists of two parts: a two-step training protocol based on homomorphic addition and a zero knowledge proof for data validity. By combining these two techniques, our framework provides privacy of per-user data, prevents against a malicious user contributing corrupted data to the shared pool, enables each user to self-compute the results of the algorithm without relying on external trusted third parties, and requires no private channels between groups of users. We show how different ML algorithms such as Latent Dirichlet Allocation, Naive Bayes, Decision Trees etc. fit our framework for distributed, secure computing.
Muhammad Saad, Laurent Njilla, Charles Kamhoua, Aziz Mohaisen
Selfish mining is a well known vulnerability in blockchains exploited by miners to steal block rewards. In this paper, we explore a new form of selfish mining attack that guarantees high rewards with low cost. We show the feasibility of this attack facilitated by recent developments in blockchain technology opening new attack avenues. By outlining the limitations of existing countermeasures, we highlight a need for new defense strategies to counter this attack, and leverage key system parameters in blockchain applications to propose an algorithm that enforces fair mining. We use the expected transaction confirmation height and block publishing height to detect selfish mining behavior and develop a network-wide defense mechanism to disincentivize selfish miners. Our design involves a simple modifications to transactions' data structure in order to obtain a “truth state” used to catch the selfish miners and prevent honest miners from losing block rewards.
Muhammad Shayan, Clement Fung, Chris J. M. Yoon, Ivan Beschastnikh
Federated Learning is the current state of the art in supporting secure multi-party machine learning (ML): data is maintained on the owner's device and the updates to the model are aggregated through a secure protocol. However, this process assumes a trusted centralized infrastructure for coordination, and clients must trust that the central service does not use the byproducts of client data. In addition to this, a group of malicious clients could also harm the performance of the model by carrying out a poisoning attack. As a response, we propose Biscotti: a fully decentralized peer to peer (P2P) approach to multi-party ML, which uses blockchain and cryptographic primitives to coordinate a privacy-preserving ML process between peering clients. Our evaluation demonstrates that Biscotti is scalable, fault tolerant, and defends against known attacks. For example, Biscotti is able to protect the privacy of an individual client's update and the performance of the global model at scale when 30% of adversaries are trying to poison the model. The implementation can be found at: https://github.com/DistributedML/Biscotti
Token Curated Registries (TCR) are decentralized recommendation systems that can be implemented using Blockchain smart contracts. They allow participants to vote for or against adding items to a list through a process that involves staking tokens intrinsic to the registry, with winners receiving the staked tokens for each vote. A TCR aims to provide incentives to create a well-curated list. In this work, we consider a challenge for these systems - incentivizing token-holders to actually engage and participate in the voting process. We propose a novel token-inflation mechanism for enhancing engagement, whereby only voting participants see their token supply increased by a pre-defined multiple after each round of voting. To evaluate this proposal, we propose a simple 4-class model of voters that captures all possible combinations of two key dimensions: whether they are engaged (likely to vote at all for a given item) or disengaged, and whether they are informed (likely to vote in a way that increases the quality of the list) or uninformed, and a simple metric to evaluate the quality of the list as a function of the vote outcomes. We conduct simulations using this model of voters and show that implementing token-inflation results in greater wealth accumulation for engaged voters. In particular, when the number of informed voters is sufficiently high, our simulations show that voters that are both informed and engaged see the greatest benefits from participating in the registry when our proposed token-inflation mechanism is employed. We further validate this finding using a simplified mathematical analysis.
We compute the revenue ratio of the Trail Stubborn mining strategy in the Bitcoin network and compare its profitability to other block-withholding strategies. We use for this martingale techniques and a classical analysis of the hiker problem. In this strategy the attacker could find himself mining in a shorter fork, but we prove that for some parameter values it is still profitable to not give up. This confirms previous numerical studies.
Although Bitcoin was intended to be a decentralized digital currency, in practice, mining power is quite concentrated. This fact is a persistent source of concern for the Bitcoin community. We provide an explanation using a simple model to capture miners' incentives to invest in equipment. In our model, $n$ miners compete for a prize of fixed size. Each miner chooses an investment $q_i$, incurring cost $c_i q_i$, and then receives reward $\frac{q_i^α}{\sum_j q_j^α}$, for some $α\geq 1$. When $c_i = c_j$ for all $i,j$, and $α= 1$, there is a unique equilibrium where all miners invest equally. However, we prove that under seemingly mild deviations from this model, equilibrium outcomes become drastically more centralized. In particular, (a) When costs are asymmetric, if miner $i$ chooses to invest, then miner $j$ has market share at least $1-\frac{c_j}{c_i}$. That is, if miner $j$ has costs that are (e.g.) $20\%$ lower than those of miner $i$, then miner $j$ must control at least $20\%$ of the \emph{total} mining power. (b) In the presence of economies of scale ($α> 1$), every market participant has a market share of at least $1-\frac{1}α$, implying that the market features at most $\fracα{α- 1}$ miners in total. We discuss the implications of our results for the future design of cryptocurrencies. In particular, our work further motivates the study of protocols that minimize "orphaned" blocks, proof-of-stake protocols, and incentive compatible protocols.
A blockchain system is a replicated state machine that must be fault tolerant. When designing a blockchain system, there is usually a trade-off between decentralization, scalability, and security. In this paper, we propose a novel blockchain system, DEXON, which achieves high scalability while remaining decentralized and robust in the real-world environment. We have two main contributions. First, we present a highly scalable sharding framework for blockchain. This framework takes an arbitrary number of single chains and transforms them into the \textit{blocklattice} data structure, enabling \textit{high scalability} and \textit{low transaction confirmation latency} with asymptotically optimal communication overhead. Second, we propose a single-chain protocol based on our novel verifiable random function and a new Byzantine agreement that achieves high decentralization and low latency.
As a disruptive technology, blockchain, particularly its original form of bitcoin as a type of digital currency, has attracted great attentions. The innovative distributed decision making and security mechanism lay the technical foundation for its success, making us consider to penetrate the power of blockchain technology to distributed control and cooperative robotics, in which the distributed and secure mechanism is also highly demanded. Actually, security and distributed communication have long been unsolved problems in the field of distributed control and cooperative robotics. It has been reported on the network failure and intruder attacks of distributed control and multi-robotic systems. Blockchain technology provides promise to remedy this situation thoroughly. This work is intended to create a global picture of blockchain technology on its working principle and key elements in the language of control and robotics, to provide a shortcut for beginners to step into this research field.
We increasingly live in a world where there is a balance between the rights to privacy and the requirements for consent, and the rights of society to protect itself. Within this world, there is an ever-increasing requirement to protect the identities involved within financial transactions, but this makes things increasingly difficult for law enforcement agencies, especially in terms of financial fraud and money laundering. This paper reviews the state-of-the-art in terms of the methods of privacy that are being used within cryptocurrency transactions, and in the challenges that law enforcement face.
Zhengwei Ni, Wenbo Wang, Dong In Kim, Ping Wang · 5 authors
With the development of decentralized consensus protocols, permissionless blockchains have been envisioned as a promising enabler for the general-purpose transaction-driven, autonomous systems. However, most of the prevalent blockchain networks are built upon the consensus protocols under the crypto-puzzle framework known as proof-of-work. Such protocols face the inherent problem of transaction-processing bottleneck, as the networks achieve the decentralized consensus for transaction confirmation at the cost of very high latency. In this paper, we study the problem of consensus formation in a system of multiple throughput-scalable blockchains with sharded consensus. Specifically, the protocol design of sharded consensus not only enables parallelizing the process of transaction validation with sub-groups of processors, but also introduces the Byzantine consensus protocols for accelerating the consensus processes. By allowing different blockchains to impose different levels of processing fees and to have different transaction-generating rate, we aim to simulate the multi-service provision eco-systems based on blockchains in real world. We focus on the dynamics of blockchain-selection in the condition of a large population of consensus processors. Hence, we model the evolution of blockchain selection by the individual processors as an evolutionary game. Both the theoretical and the numerical analysis are provided regarding the evolutionary equilibria and the stability of the processors' strategies in a general case.
In 2008, Satoshi Nakamoto proposed an electronic cash system (bitcoin) that is completely realized by peer-to-peer technology. The core value of this scheme is that it proposes a solution based on Proof-of Work, so that the cash system can run in a peer-to-peer environment and be able to prevent double-spend attacks. Bitcoin has been developed for ten years, and since then countless digital currencies have been created. But the discussion of double-spend attacks seems to still concentrate on 51% Attacks. In fact, our research has found that there are many other way to achieve double-spend attacks. In this paper, by introducing a number of double-spend attack vulnerabilities that we have found in EOS, NEO and other large blockchain platforms, we summarized various reasons for causing double-spend attacks, and propose an efficient mitigation measure against them.
Wesley Joon-Wie Tann, Xing Han, Sourav Sen Gupta, Yew-Soon Ong
Symbolic analysis of security exploits in smart contracts has demonstrated to be valuable for analyzing predefined vulnerability properties. While some symbolic tools perform complex analysis steps, they require a predetermined invocation depth to search vulnerable execution paths, and the search time increases with depth. The number of contracts on blockchains like Ethereum has increased 176 fold since December 2015. If these symbolic tools fail to analyze the increasingly large number of contracts in time, entire classes of exploits could cause irrevocable damage. In this paper, we aim to have safer smart contracts against emerging threats. We propose the approach of sequential learning of smart contract weaknesses using machine learning---long-short term memory (LSTM)---that allows us to be able to detect new attack trends relatively quickly, leading to safer smart contracts. Our experimental studies on 620,000 smart contracts prove that our model can easily scale to analyze a massive amount of contracts; that is, the LSTM maintains near constant analysis time as contracts increase in complexity. In addition, our approach achieves $99\%$ test accuracy and correctly analyzes contracts that were false positive (FP) errors made by a symbolic tool.
Recently, Autonomous Vehicles (AVs) have gained extensive attention from both academia and industry. AVs are a complex system composed of many subsystems, making them a typical target for attackers. Therefore, the firmware of the different subsystems needs to be updated to the latest version by the manufacturer to fix bugs and introduce new features, e.g., using security patches. In this paper, we propose a distributed firmware update scheme for the AVs' subsystems, leveraging blockchain and smart contract technology. A consortium blockchain made of different AVs manufacturers is used to ensure the authenticity and integrity of firmware updates. Instead of depending on centralized third parties to distribute the new updates, we enable AVs, namely distributors, to participate in the distribution process and we take advantage of their mobility to guarantee high availability and fast delivery of the updates. To incentivize AVs to distribute the updates, a reward system is established that maintains a credit reputation for each distributor account in the blockchain. A zero-knowledge proof protocol is used to exchange the update in return for a proof of distribution in a trust-less environment. Moreover, we use attribute-based encryption (ABE) scheme to ensure that only authorized AVs will be able to download and use a new update. Our analysis indicates that the additional cryptography primitives and exchanged transactions do not affect the operation of the AVs network. Also, our security analysis demonstrates that our scheme is efficient and secure against different attacks.
The choice of the consensus method ultimately determines throughput, scalability, tamper resistance, and consistency of a blockchain system. However, across all the types of blockchain (private, semi-private, consortium, or public), there is no consensus method that uniformly addresses all these traits. Verifiable lottery algorithms (Proof of ...) increase tamper resistance but show weakness in throughput and scalability, while established methods like PAXOS and RAFT provide no additional protection against tampering. In this paper, we introduce Right to Sign which aims to provide additional tamper resistance by cryptographic signatures over a broad range of available consensus finding methods.
Summary Decentralization, in the form of mesh networking and blockchain, two promising technologies, is coming to the telecommunications industry. Mesh networking allows wider low‐cost Internet access with infrastructures built from routers contributed by diverse owners, whereas blockchain enables transparency and accountability for investments, revenue, or other forms of economic compensations from sharing of network traffic, content, and services. Crowdsourcing network coverage, combined with crowdfunding costs, can create economically sustainable yet decentralized Internet access. This means that every participant can invest in resources and pay or be paid for usage to recover the costs of network devices and maintenance. While mesh networks and mesh routing protocols enable self‐organized networks that expand organically, cryptocurrencies and smart contracts enable the economic coordination among network providers and consumers. We explore and evaluate two existing blockchain software stacks, Hyperledger Fabric (HLF) and Ethereum geth with Proof of Authority (PoA) intended as a local lightweight distributed ledger, deployed in a real city‐wide production mesh network and in laboratory network. We quantify the performance and bottlenecks and identify the current limitations and opportunities for improvement to serve locally the needs of wireless mesh networks, without the privacy and economic cost of relying on public blockchains.
Zijian Bao, Bin Wang, Yongxin Zhang, Qinghao Wang · 5 authors
We propose Lockcoin, a secure and privacy-preserving mix service for bitcoin anonymity. We introduce mix servers to provide mix service for user to prevent attackers linking the input address with output address by using blind signature shceme, multisignature scheme. Lockcoin provides anonymity, scalability, bitcoin compatibillity, theft impossibility and accountability. We have proposed a prototype of Lockcoin based on bitcoin test network, experimental results show that our solution is efficient. Lockcoin's source codes are released on github.com/Northeastern-University-Blockchain/Lockcoin.
One major shortcoming of permissionless blockchains such as Bitcoin and Ethereum is that they are unsuitable for running Computationally Intensive smart Contracts (CICs). This prevents such blockchains from running Machine Learning algorithms, Zero-Knowledge proofs, etc. which may need non-trivial computation. In this paper, we present YODA, which is to the best of our knowledge the first solution for efficient computation of CICs in permissionless blockchains with guarantees for a threat model with both Byzantine and selfish nodes. YODA selects one or more execution sets (ES) via Sortition to execute a particular CIC off-chain. One key innovation is the MultI-Round Adaptive Consensus using Likelihood Estimation (MIRACLE) algorithm based on sequential hypothesis testing. M I RACLE allows the execution sets to be small thus making YODA efficient while ensuring correct CIC execution with high probability. It adapts the number of ES sets automatically depending on the concentration of Byzantine nodes in the system and is optimal in terms of the expected number of ES sets used in certain scenarios. Through a suite of economic incentives and technical mechanisms such as the novel Randomness Inserted Contract Execution (RICE) algorithm, we force selfish nodes to behave honestly. We also prove that the honest behavior of selfish nodes is an approximate Nash Equilibrium. We present the system design and details of YODA and prove the security properties of MIRACLE and RICE. Our prototype implementation built on top of Ethereum demonstrates the ability of YODA to run CICs with orders of magnitude higher gas per unit time as well as total gas requirements than Ethereum currently supports. It also demonstrates the low overheads of RICE.
Jingwei Liu, Xiaolu Li, Lin Ye, Hongli Zhang · 6 authors
Electronic medical record (EMR) is a crucial form of healthcare data, currently drawing a lot of attention. Sharing health data is considered to be a critical approach to improve the quality of healthcare service and reduce medical costs. However, EMRs are fragmented across decentralized hospitals, which hinders data sharing and puts patients' privacy at risks. To address these issues, we propose a blockchain based privacy-preserving data sharing for EMRs, called BPDS. In BPDS, the original EMRs are stored securely in the cloud and the indexes are reserved in a tamper-proof consortium blockchain. By this means, the risk of the medical data leakage could be greatly reduced, and at the same time, the indexes in blockchain ensure that the EMRs can not be modified arbitrarily. Secure data sharing can be accomplished automatically according to the predefined access permissions of patients through the smart contracts of blockchain. Besides, the joint-design of the CP-ABE-based access control mechanism and the content extraction signature scheme provides strong privacy preservation in data sharing. Security analysis shows that BPDS is a secure and effective way to realize data sharing for EMRs.