Smart contracts on blockchain systems implement business logic and directly handle important assets. Although smart contracts play these critical roles, it is hard for users interacting with the system to understand the real behavior of the deployed bytecodes of smart contracts. The quirks of smart contracts, such as code reuse and limited unique datasets, make it challenging to recognize the functional details of smart contracts. In this paper, we propose a new method for characterizing bytecode-only smart contracts by automatically assigning multiple attribute tags. Using a deep learning approach, our system, the ScanAT, extracts attribute tags from the source code and metadata of known smart contracts and trains their bytecode with the attribute tags. The ScanAT then infers attribute tags from the bytecode of smart contracts alone. Our experiments show that ScanAT can achieve 81% accuracy in predicting attribute tags, using convolutional neural networks and a customized autoencoder.
Zhiyi Zhang, Vishrant Vasavada, Randy King, Lixia Zhang
Over the last few years, blockchain-based technologies have flourished in many application areas. One of them is the creation of distributed ledgers where records of immutable objects are widely replicated for both transparency and availability. However, the Proof-of-Work (PoW) approach, a popular gating control that determines who can add new records into a ledger, is deemed infeasible for IoT devices with resource constraints.
Yonggen Gu, Dingding Hou, Xiaohong Wu, Jie Tao · 5 authors
Distributed data storage has received more attention due to its advantages in reliability, availability and scalability, and it brings both opportunities and challenges for distributed data storage transaction. The traditional transaction system of storage resources, which generally runs in a centralized mode, results in high cost, vendor lock-in and single point failure risk. To overcome the above shortcomings, considering the storage policy with erasure coding, in this paper we propose a decentralized transaction method for cloud storage based on a smart contract, which takes into account the resource cost for distributed data storage. First, to guarantee the availability and decrease the storing cost, a reverse Vickrey-Clarke-Groves (VCG) based auction mechanism is proposed for storage resource selection and transaction. Then we deploy and implement the proposed mechanism by designing a corresponding smart contract. Especially, we address the problem of how to implement a VCG-like mechanism in a blockchain environment. Based on the private chain of Ethereum, we make the simulation for the proposed storage transaction method. The results of simulation show that the proposed transaction model can realize competitive trading of storage resources and ensure the safe and economic operation of resource trading.
We investigate a family of bugs in blockchain-based smart contracts, which we call event-ordering (or EO) bugs. These bugs are intimately related to the dynamic ordering of contract events, i.e., calls of its functions on the blockchain, and enable potential exploits of millions of USD worth of Ether. Known examples of such bugs and prior techniques to detect them have been restricted to a small number of event orderings, typicall 1 or 2. Our work provides a new formulation of this general class of EO bugs as finding concurrency properties arising in long permutations of such events. The technical challenge in detecting our formulation of EO bugs is the inherent combinatorial blowup in path and state space analysis, even for simple contracts. We propose the first use of partial-order reduction techniques, using happen-before relations extracted automatically for contracts, along with several other optimizations built on a dynamic symbolic execution technique. We build an automatic tool called ETHRACER that requires no hints from users and runs directly on Ethereum bytecode. It flags 7-11% of over ten thousand contracts analyzed in roughly 18.5 minutes per contract, providing compact event traces that human analysts can run as witnesses. These witnesses are so compact that confirmations require only a few minutes of human effort. Half of the flagged contracts have subtle EO bugs, including in ERC-20 contracts that carry hundreds of millions of dollars worth of Ether. Thus, ETHRACER is effective at detecting a subtle yet dangerous class of bugs which existing tools miss.
Traditionally, the Net Present Value method has been used to compare diverging investment strategies. However, valuating crypto-projects with fiat-based currency is confusing due to extreme coin appreciation rates as compared to fiat interest rates. Here, we provide a net present value method based on using crypto-coin as the underlying asset. Using this method, we compare buy-and-hold versus mine-and-hold; we also provide a sensitivity analysis of profitability.
Seo-Joon Lee, Gyoun-Yon Cho, Fumiaki Ikeno, Tae-Ro Lee
Due to the development of high-throughput DNA sequencing technology, genome-sequencing costs have been significantly reduced, which has led to a number of revolutionary advances in the genetics industry. However, the problem is that compared to the decrease in time and cost needed for DNA sequencing, the management of such large volumes of data is still an issue. Therefore, this research proposes Blockchain Applied FASTQ and FASTA Lossless Compression (BAQALC), a lossless compression algorithm that allows for the efficient transmission and storage of the immense amounts of DNA sequence data that are being generated by Next Generation Sequencing (NGS). Also, security and reliability issues exist in public sequence databases. For methods, compression ratio comparisons were determined for genetic biomarkers corresponding to the five diseases with the highest mortality rates according to the World Health Organization. The results showed an average compression ratio of approximately 12 for all the genetic datasets used. BAQALC performed especially well for lung cancer genetic markers, with a compression ratio of 17.02. BAQALC performed not only comparatively higher than widely used compression algorithms, but also higher than algorithms described in previously published research. The proposed solution is envisioned to contribute to providing an efficient and secure transmission and storage platform for next-generation medical informatics based on smart devices for both researchers and healthcare users.
Open access
Blockchain Technology Applications and Security
Innovative Microfluidic and Catalytic Techniques Innovation
Aug 1, 2018·2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE)
Saqib Ali, Guojun Wang, Bebo White, Roger Leslie Cottrell
The blockchain is an innovative technology which opened doors to new applications for solving numerous problems in distributed environments. In this work, we design a blockchain-based data storage and access framework for PingER (worldwide end-to-end Internet performance measurement project) to remove its total dependence on a centralized repository. We use the permissioned blockchain and Distributed Hash Tables (DHT) for this purpose. In the proposed framework, metadata of the files are stored on the blockchain whereas the actual files are stored off-chain through DHT at multiple locations using a peer-to-peer network of PingER Monitoring Agents. This will provide decentralized storage, distributed processing, and efficient lookup capabilities to the PingER framework.
When dot.com has become a quaint idea, when electronic shops have lost the mass attention, while classical and margin trading has become obsolete, something new is coming: cryptocurrencies. Hundreds of virtual coins have been invented for a single reason: the profit. The high price volatility of these new markets and the fact that the virtual coins price is not regulated by a central bank or a single exchange, gives us opportunities for arbitrage trading. The existence of important price differences makes possible the profit when an automated system buy cheaper and sell more expensive in the same time. This paper will present the general principles underpinning the implementation of arbitrage trading software for virtual coins market. The very large number of cryptocurrencies and exchanges fundamentally change the server architecture of the trading software. The distributed price data in hundreds of sources and the technical differences of each of these data providers make all the things difficult to be implemented in a single application. The low-latency order calculation needed for the fast delivery before a significant price change, in the presence of thousands of price quotes coming from hundreds of distributed servers makes everything special.
Die Preisvorhersage ist eine der größten Herausforderungen der quantitativen Finanzierung. Diese Thesis stellt ein Neuronales Netz-Framework vor, das eine tiefgreifende maschinelle Lernlösung für das Preisvorhersageproblem bietet. Das Framework wird in drei Zeitpunkten mit einem Multilayer Perzeptron (MLP), einem einfachen Recurrent Neural Network (RNN) und einem Long Short Term Memory (LSTM) realisiert, die lange Abhängigkeiten lernen können. Wir beschreiben die Theorie der neuronalen Netze und des Deep Learning, um eine reproduzierbare Methode für unsere Anwendungen auf dem Kryptowährungsmarkt zu erstellen. Da die Preisvorhersage verwendet wird, um finanzielle Entscheidungen wie Handelssignale zu treffen, vergleichen wir verschiedene Ansätze des Vorhersageproblems, indem wir überwachte Lernmethoden in Klassifikationsaufgaben untersuchen. Wir untersuchen diese Modelle, um Preisrichtungen von wichtitgen Kryptowährungen außerhalb der Stichprobe mit einer rolling window regression Methode vorherzusagen. Für dieses Ziel erstellen wir ein Klassifikationsproblem, das voraussagt, ob der Preis jeder Kryptowährung als Grundlage für dreimonatige Handelsstrategien erheblich zu- oder abnimmt. Wir bauen verschiedene Handelsstrategien auf, basierend auf Long- oder Long- / Short-Positionen, die auf unseren Prognosen aufbauen, und vergleichen ihre Performance mit einer passiven Index-Investition auf dem Cryptowährungsmarkt, die CRIX (Trimborn and Härdle, 2016) folgt. Cryptocurrencies, Bitcoins sind die bekanntesten, basieren auf elektronischem Geld auf Blockchain-Technologie, die als eine dezentrale Alternative zu Währungen verwendet werden kann. Dank ihrer zahlreichen Anwendungen hat der Markt für Kryptowährung im Jahr 2017 ein exponentielles Wachstum erfahren. Wir vergleichen verschiedene gewichtete Portfolios, um zu testen, wie ein Anleger von fundamentalen Indikatoren wie der Marktkapitalisierung profitieren kann. Wir finden, dass LST die beste Genauigkeit für die Vorhersage von Richtungsbewegungen für die wichtigsten Kryptowährungen von CRIX hat und dass ein gleich gewichtetes Portfolio CRIX in den ersten Quartalen 2017 schlägt.
This thesis focuses on the integration of smart contracts into the Bazo Blockchain with the goal of creating a platform for decentralized applications. Smart contracts are programs that are stored on a blockchain and can be triggered by transactions. Smart contracts offer opportunities in automation and bring along advantages provided by blockchains such as immutability, public visibility of transactions and decentralization. As blockchains are trust-less protocols, it is guar- anteed that smart contracts are executed as intended. The integration of smart contracts was solved by implementing a virtual machine that executes the instructions sent by a transaction. This stack based virtual machine uses byte arrays as base type, which enables the virtual machine to deal with numbers of arbitrary length. Working with elements of arbitrary size requires taking the length of the elements into account, when calculating the gas cost, in addition to the base cost of the instruction type. Furthermore, the virtual machine was embedded within the mining application, which required to alter the blockchain protocol and the mining application. As a result, smart contracts can be deployed and transactions that call functions of smart contracts can be executed. Calling a smart contract function leads to the execution of the contract in the virtual machine and persisting the result in the blockchain. The Bazo Blockchain contin- ues to be a research project, this means it’s not ready for production use due to complex setup and handling. Follow-up theses could simplify the development of smart contracts for the Bazo Blockchain by creating a high-level programming language that can be compiled to Bazo virtual machine instructions.
The rise in popularity of permissioned blockchain platforms in recent time is significant. Hyperledger Fabric is one such permissioned blockchain platform and one of the Hyperledger projects hosted by the Linux Foundation. The Fabric comprises various components such as smart-contracts, endorsers, committers, validators, and orderers. As the performance of blockchain platform is a major concern for enterprise applications, in this work, we perform a comprehensive empirical study to characterize the performance of Hyperledger Fabric and identify potential performance bottlenecks to gain a better understanding of the system. We follow a two-phased approach. In the first phase, our goal is to understand the impact of various configuration parameters such as block size, endorsement policy, channels, resource allocation, state database choice on the transaction throughput & latency to provide various guidelines on configuring these parameters. In addition, we also aim to identify performance bottlenecks and hotspots. We observed that (1) endorsement policy verification, (2) sequential policy validation of transactions in a block, and (3) state validation and commit (with CouchDB) were the three major bottlenecks. In the second phase, we focus on optimizing Hyperledger Fabric v1.0 based on our observations. We introduced and studied various simple optimizations such as aggressive caching for endorsement policy verification in the cryptography component (3x improvement in the performance) and parallelizing endorsement policy verification (7x improvement). Further, we enhanced and measured the effect of an existing bulk read/write optimization for CouchDB during state validation & commit phase (2.5x improvement). By combining all three optimizations1, we improved the overall throughput by 16x (i.e., from 140 tps to 2250 tps).
Doriane Perard, Jérôme Lacan, Yann Bachy, Jonathan Detchart
The concept of a decentralized ledger usually implies that each node of a blockchain network stores the entire blockchain. However, in the case of popular blockchains, which each weigh several hundreds of GB, the large amount of data to be stored can incite new or low-capacity nodes to run lightweight clients. Such nodes do not participate to the global storage effort and can result in a centralization of the blockchain by very few nodes, which is contrary to the basic concepts of a blockchain. To avoid this problem, we propose new low storage nodes that store a reduced amount of data generated from the blockchain by using erasure codes. The properties of this technique ensure that any block of the chain can be easily rebuild from a small number of such nodes. This system should encourage low storage nodes to contribute to the storage of the blockchain and to maintain decentralization despite of a globally increasing size of the blockchain. This system paves the way to new types of blockchains which would only be managed by low capacity nodes.
Decentralized currencies and similar blockchain applications require consensus. Bitcoin achieves eventual consensus in a fully-decentralized setting, but provides very low throughput and high latency with excessive energy consumption. In this paper, we propose identity aging as a novel and more efficient consensus approach. Our main idea is to establish reliable, long-term identities and choose the oldest identity as the miner on each round. Based on this approach, we design two blockchain systems. Our first system, SCIFER, leverages Intel's SGX attestation for identity bootstrapping in a partially-decentralized setting, where blockchain is permissionless, but we trust Intel for attestation. Our second system, DIFER, creates new identities through a novel mining mechanism and provides consensus in a fully-decentralized setting, similar to Bitcoin. One of the main benefits of identity aging is that it does not require constant computation. Our analysis and experiments show that identity aging provides significant performance improvements over Bitcoin with strong security guarantees.
The importance of big data in machine learning cannot be overemphasized in recent times. Through the evolution of big data, most scientific technologies that relied heavily on enormous data in solving complex issues in human lives gained grounds; machine learning is an instance of these technologies. Various machine learning models that yield groundbreaking throughputs with high efficiency rates in predicting, detecting, classifying, discovering and acquiring in-depth knowledge about events that would otherwise be very difficult to ascertain have been made possible due to big data. Although big data has undoubtedly helped in the field of machine learning research ,over the years, its mode of acquisition has posed great challenge in industries,education and other agencies that obtained them for various purposes. This is because these large quantities of data cannot be stored on personal computers with limited storage capabicity but required the use of high storage capacity servers for effective storage. These servers may be owned by a group of companies or individuals who had the singular priviledge to modify the data in their possession as and when deemed relevant thus the creation of a centralized data storage environment. These were mostly refered to as the Third Parties (TP) in the data acquisition process. For the services they rendered, these trusted parties priced data in their possession expensively. The adverse effect is a limitation on various researches that could help solve a number of problems in human lives. It is worth mentioning that the security of these data being purchased expensively cannot be even assured limiting various researches that thrive on secured data. In order to curb these occurrences and have better machine learning models, the incorporation of Blockchain Technology databases into machine learning. This paper discusses the concept of big data, Machine Learning and Blockchains. It further discusses how Big data has impacted the Machine learning Community, the significance of Machine Learning and how the BlockChain Technology could be used similarly impact the Machine Learning Community. The aim of this paper is to encourge further research in incoporating the BlockChain Technology into Machine Learning.
Initial Coin Offerings (ICO) are public offers of new cryptocurrencies in exchange of existing ones, aimed to finance projects in the blockchain development arena. In the last 8 months of 2017, the total amount gathered by ICOs exceeded 4 billion US$, and overcame the venture capital funnelled toward high tech initiatives in the same period. A high percentage of ICOS is managed through Smart Contracts running on Ethereum blockchain, and in particular to ERC-20 Token Standard Contract. In this work we examine 1388 ICOs, published on December 31, 2017 on icobench.com Web site, gathering information relevant to the assessment of their quality and software development management, including data on their development teams. We also study, at the same date, the financial data of 450 ICO tokens available on coinmarketcap.com Web site, among which 355 tokens are managed on Ethereum blochain. We define success criteria for the ICOs, based on the funds actually gathered, and on the behavior of the price of the related tokens, finding the factors that most likely influence the ICO success likeliness.
Sheng Wang, Tien Tuan Anh Dinh, Qian Lin, Zhongle Xie · 10 authors
Existing data storage systems offer a wide range of functionalities to accommodate an equally diverse range of applications. However, new classes of applications have emerged, e.g., blockchain and collaborative analytics, featuring data versioning, fork semantics, tamper-evidence or any combination thereof. They present new opportunities for storage systems to efficiently support such applications by embedding the above requirements into the storage. In this paper, we present ForkBase, a storage engine specifically designed to provide efficient support for blockchain and forkable applications. By integrating the core application properties into the storage, ForkBase not only delivers high performance but also reduces development effort. Data in ForkBase is multi-versioned, and each version uniquely identifies the data content and its history. Two variants of fork semantics are supported in ForkBase to facilitate any collaboration workflows. A novel index structure is introduced to efficiently identify and eliminate duplicate content across data objects. Consequently, ForkBase is not only efficient in performance, but also in space requirement. We demonstrate the performance of ForkBase using three applications: a blockchain platform, a wiki engine and a collaborative analytics application. We conduct extensive experimental evaluation of these applications against respective state-of-the-art system. The results show that ForkBase achieves superior performance while significantly lowering the development cost.
Derrick Chan, R. O. Decker, William Nguyen, Yuya Oguchi
Cryptocurrencies are rapidly receiving mainstream attention and adoption. As a result, new technologies are developing to store and transact cryptocurrencies to keep up with a rapidly developing market. We have developed a way to utilize a mobile phone to communicate with the Ethereum blockchain in order to monitor and initiate cryptocurrency transactions. The project allows users to create new Ethereum addresses and to send arbitrary amounts of Ether to existing Ethereum addresses, while maintaining security for the end user. We conclude that this method of communication with the Ethereum blockchain is viable and is an avenue for exploration in the future. As a result from this project, we anticipate a growth of new cryptocurrency applications on mobile devices. Furthermore, the various security features utilized in this project such as pin randomization, mnemonic recovery, and hardware level encryption can be implemented in other applications to increase security for all users.
Cryptocurrencies has become a very hot topic recently, with Bitcoin being the most popular. The increase in interest has led to an incentive to create payment systems for the currency that makes it easier to use for day-to-day shopping. A lot of companies are inves- tigating possible solutions for credit cards that are used for cryptocurrencies. This thesis aims to present and perform a security analysis on an already created concept of a credit card payment system for Bitcoin. The security analysis is done in a systematical approach where the modules were analyzed with predetermined restrictions and assumptions. The restricitons and assumptions are then removed one-by-one to find potential threats in the system. The outcome of the analysis is then evaluated in an attempt to find possible im- plementation methods that would mitigate or prevent the discovered threats. The possible implementations are also evaluated in terms of how they would affect the system.
With increasing popularity, the scalability of blockchain-based crypto-currencies becomes an urgent concern. Bitcoin, among other crypto-currencies, have known limitations in transaction throughput and speedbut no agreed upon solution. Many of the proposals increasing throughput also increase the load on the Bitcoin network. Lightning is a proposal enabling payments to be conducted without affecting the Bitcoin network. Lightning is a protocol using payment channels to enable instant and cheap payments using the blockchain for security. With Lightning, payments can be sent through a network of nodes connected by payment channels. In this thesis, the current state of Lightning is explored and an evaluation for use on Bitcoin exchanges is performed. The usability, privacy and security of Lightning are the topics evaluated. In order to evaluate the usability, an exchange interface application was built allowing deposits and withdrawals using Lightning. The protocol specification and relevant literature are examined in order to evaluate the security and privacy of Lightning. The exchange interface showed that Lightning can be integrated into an exchange and provide a fast and cheap way to deposit and withdraw money for clients. In this paper some integration designs are proposed and ways to improve the client user experience are discussed. The Lightning node implementations need to mature a bit before real usage and there are some security issues that also need to be considered. With monitoring and network analysis the privacy can be compromised.
Online and offline storage of digital currency present conflicting risks for a Bitcoin exchange. While bitcoins stored on online devices are continually vulnerable to malware and other network-based attacks, offline reserves are endangered on access, as transferring bitcoins requires the exposure of otherwise encrypted and secured private keys. In particular, fluctuations in customer demand for deposited bitcoin require exchanges to periodically refill online storage systems with bitcoins held offline. This raises the natural question of what upper limit on online reserves minimizes losses due to theft over time. In this article, we investigate this optimization problem, developing a model that predicts the optimal ceiling on online reserves, given average rates of deposits, withdrawals, and theft. We evaluate our theory with an event-driven simulation of the setup, and find that our equation yields a numerical value for the threshold that differs by less than 2% from experimental results. We conclude by considering open questions regarding more complex storage architectures.
The recent emergence of a distributed technology named blockchain, clearly created a new point of view in the data storing and data distribution fields. If on one hand blockchain is mainly known for Bitcoin (an auto-regulated decentralized digital currency), on the other hand it has the potential to set up an auto regulated economy.In this thesis, the blockchain technology will be analyzed and described starting from P2P architecture and its origin in 2009 Satoshi Nakamoto’s whitepaper, and leading to the most up to date blockchains. The advantages and disadvantages of such architecture will be pointed out keeping in mind the security, speed and cost of such infrastructure.While Real Estate companies have often anticipated the technological innovations, land registries, instead, derive and keep a working manner which is extremely old and out of date: made of unclear procedures and wet signatures. The market needs and legislation will be researched mainly referring to other works and integrated with a technical point of view with particular focus on the decentralization of such systems.After analyzing the flow, problems and flaws of the current system, a new proposal will be researched, in particular trying to minimize the dead time in between the different steps of the mortgage, increase transparency, as well as reducing dependence on the central authorities, leading to more convenient interactions among the properties’ stakeholders. An attractive low capitalization decentralized financial product will also be proposed and implemented able to lower the interest rate and create a profitable investment with low risk, low interest and durable in time.Secure and ad-hoc algorithms will be presented and, in a later section, analyzed in combination with different blockchain technologies. Scalability and performance will also be evaluated, taking into account all the current technology limitations and the near future opportunities.
This thesis is one in a group of several theses that are researching different subjects in the development of a new cryptocurrency. For a few years now, the cryptocurrency market has grown dramatically, in the lead of the original cryptocurrency Bitcoin. Today, most cryptocurrencies' validation-technology, including Bitcoin's, are based on Proof-of-Work (PoW), i.e., a system where transaction validation is made by servers calculating mathematical problems. PoW results in high energy consumption and slow transaction speed. In this cryptocurrency, the validation mechanism will build on a technology called Proof-of-Stake (PoS). PoS does not yield as high energy consumption and often leads to faster transaction speed. The specific technique for validation in this system is that validators bet their coins to validate transactions and get rewards in the form of transaction fees if they end up conforming the transactions that reach consensus among the validators. In particular, the purpose of this report is to research the risk and reward for validators in the betting process and from this develop a reward policy which yields a fast and secure validation. The methods used for solving the problems are simulations based on Monte Carlo methods. From the simulations, the results are discussed and compared. Also, this report will cover economic theories behind cryptocurrencies, mainly focusing on monetary policy and the transaction markets. The findings of this report are several risk functions for different topologies and winning conditions considered during the development of the cryptocurrency. Further, a conclusion was that the expected value of profit for validators need to be constant, independent of when the bets are made with regard to previous bets. From this, a reward function which distributes rewards between winning validators was formed. Another, economical conclusion from this was that, in the long run, the expected value of profit of betting should converge to zero due to a perfect competition market.