Platforms are important actors in contemporary cultural economic processes. They include social network sites, online content management systems, streaming media platforms, mobile communication infrastructures, supply chain logistics solutions, and cryptocurrencies. Analysis of platforms and their capitalization should take into account the ways they structure social practice as assets and the constitutive opacity of platforms as configured realities. It explores capitalization by focusing on the problems of counting people and things on platforms. Via a case study of the software repository platform [Github.com] (https://github.com), it analyzes how 'platform numbers’ participate in capitalization. It describes attempts to enumerate the elements of the platform by counting, mapping or listing them. The paper shows how attempts to enumerate people and things encounter forms of association, duplication, combination, imitation and configuration that are crucial to the ensemble but remain refractory to capitalization. It proposes configurative enumeration of the platform numbers as a way of conceptualizing these un-enacted excesses. In a configurative enumeration, the composition, the rhythms of imitation, variation and commutation, and constant relating, repairing and adjusting of configurations crucial to the ongoing formation of platforms come into view. Configurative enumerations engage the inventive realities of platformization, realities that precede and sometimes overflow their capitalization.
Payment channels secured with cryptocurrency as collateral enable users to make many transactions with few blockchain broadcasts. Networks of payment channels have emerged as a proposed solution to Bitcoin’s scaling problem. Since the proposal of the first payment channel network, the Lightning Network, alternatives promising significant improvements, such as the Sprites protocol, have been proposed. Without at-scale implementations to analyze in situ, it is difficult to make meaningful comparisons of payment channel network protocols. In order to bridge this gap, we introduce a new simulation framework that can be used to evaluate how different payment channel network protocols will perform in both the expected and worst cases. \n \nOur framework is generic and accommodates benchmarking across different variants of payment channel network protocols, network topologies, routing algorithms, and user behaviors. User spending behavior in our payment channel network simulator is generated based on behavioral modeling techniques used in credit card fraud research. Our simulation is the first payment channel network simulator to seed user behaviors with data from real-world credit card users. \n \nOur framework can be used to evaluate expected case performance and resiliency to attacks across different payment channel network protocols and routing algorithms. We demonstrate the utility of our framework through comparisons of the Lightning Network to Sprites. We also compare the proposed decentralized routing algorithm, Flare, to an ideal centralized routing algorithm. Our results reveal that if spending behaviors are similar to those of credit card users, scale-free network topologies achieve higher throughput and resiliency compared to small-world networks. We also confirm that the Sprites protocol enjoys numerous advantages over the Lightning Network including smaller durations, shorter path length payments, and greater resiliency, all of which are most significant in decentralized topologies using decentralized routing algorithms.
Zehui Xiong, Yang Zhang, Dusit Niyato, Ping Wang · 5 authors
Blockchain, as the backbone technology of the current popular Bitcoin digital currency, has become a promising decentralized approach for resource and transaction management. Although blockchain has been widely adopted in many applications, e.g., finance, healthcare, and logistics, its application in mobile environments is still limited. This is due to the fact that blockchain users need to solve preset proof-of-work puzzles to add new transactions to the blockchain. Solving the proof-of-work, however, consumes substantial resources in terms of CPU time and energy, which is not suitable for resource-limited mobile devices. To facilitate blockchain applications in future mobile Internet of Things systems, multiple access mobile edge computing appears to be an auspicious option to solve the proof-of-work puzzles for mobile users. We first introduce a novel concept of edge computing for mobile blockchain. Then, we introduce an economic approach for edge computing resource management. Moreover, a demonstrative prototype of mobile edge computing enabled blockchain systems is presented with experimental results to justify the proposed concept.
Zehui Xiong, Yang Zhang, Dusit Niyato, Ping Wang · 5 authors
Blockchain, as the backbone technology of the current popular Bitcoin digital currency, has become a promising decentralized data management framework. Although blockchain has been widely adopted in many applications (e.g., finance, healthcare, and logistics), its application in mobile services is still limited. This is due to the fact that blockchain users need to solve preset proof-of-work puzzles to add new data (i.e., a block) to the blockchain. Solving the proof of work, however, consumes substantial resources in terms of CPU time and energy, which is not suitable for resource-limited mobile devices. To facilitate blockchain applications in future mobile Internet of Things systems, multiple access mobile edge computing appears to be an auspicious solution to solve the proof-of-work puzzles for mobile users. We first introduce a novel concept of edge computing for mobile blockchain. Then we introduce an economic approach for edge computing resource management. Moreover, a prototype of mobile edge computing enabled blockchain systems is presented with experimental results to justify the proposed concept.
Blockchain technology can be utilized to improve gun control without changing existing laws. Firearm related mortality is at epidemic levels in the United States and not only has a significant impact upon public health, it also creates a large financial burden. Suicide is the most common way guns kill. Through better gun tracking and improved screening of high risk individuals, this technological advance in distributed ledger technology will improve background checks on individuals and tracing of guns used in crimes.
Healthcare complexity and costs can be decreased through the application of blockchain technology to medical records and insurance companies. Estonia has taken a leadership role in blockchain based services both in the commercial sector and in government. The Estonian government’s innovation strategy was to create GovTech partnerships to implement blockchain based technologies throughout the country, and become a global leader in the technology. Starting in 2011, just 3 years after Satoshi Nakamoto published the first description of distributed ledgers and blockchain technology, the Estonian Government started partnering with the private technology startup company Guardtime to use blockchains to secure public and internal records. Then in 2016, Estonia once again reinforced its global leadership in blockchain technology when it announced it would use blockchain technology to secure the health records of over a million citizens. Estonia’s systematic method of applying blockchain technologies through GovTech partnerships demnostrates how innovation is a process. Estonia also identified early the value of the blockchain as a disruptive platform innovation. The application of blockchain technology to healthcare is a radical innovation given that nearly all previous applications have been in the financial and legal sectors.
In this work, we propose a straightforward method to derive Elliptic Curve Digital Signature Algorithm (ECDSA) key pairs from embeddings created using Deep Learning and Metric Learning approaches. We also show that these keys allows the derivation of cryptocurrencies (such as Bitcoin) addresses that can be used to transfer and receive funds, allowing novel Blockchain-based applications that can be used to transfer funds or data directly to domains such as image, text, sound or any other domain where Deep Learning can extract high-quality embeddings; providing thus a novel integration between the properties of the Blockchain-based technologies such as trust minimization and decentralization together with the high-quality learned representations from Deep Learning techniques.
Brian M. Till, Alexander W. Peters, Salim Afshar, John G. Meara
Blockchain technology and cryptocurrencies could remake global health financing and usher in an era global health equity and universal health coverage. We outline and provide examples for at least four important ways in which this potential disruption of traditional global health funding mechanisms could occur: universal access to financing through direct transactions without third parties; novel new multilateral financing mechanisms; increased security and reduced fraud and corruption; and the opportunity for open markets for healthcare data that drive discovery and innovation. We see these issues as a paramount to the delivery of healthcare worldwide and relevant for payers and providers of healthcare at state, national and global levels; for government and non-governmental organisations; and for global aid organisations, including the WHO, International Monetary Fund and World Bank Group.
Shehar Bano, Alberto Sonnino, Mustafa Al-Bassam, Sarah Azouvi · 7 authors
The blockchain initially gained traction in 2008 as the technology underlying bitcoin, but now has been employed in a diverse range of applications and created a global market worth over $150B as of 2017. What distinguishes blockchains from traditional distributed databases is the ability to operate in a decentralized setting without relying on a trusted third party. As such their core technical component is consensus: how to reach agreement among a group of nodes. This has been extensively studied already in the distributed systems community for closed systems, but its application to open blockchains has revitalized the field and led to a plethora of new designs. The inherent complexity of consensus protocols and their rapid and dramatic evolution makes it hard to contextualize the design landscape. We address this challenge by conducting a systematic and comprehensive study of blockchain consensus protocols. After first discussing key themes in classical consensus protocols, we describe: first protocols based on proof-of-work (PoW), second proof-of-X (PoX) protocols that replace PoW with more energy-efficient alternatives, and third hybrid protocols that are compositions or variations of classical consensus protocols. We develop a framework to evaluate their performance, security and design properties, and use it to systematize key themes in the protocol categories described above. This evaluation leads us to identify research gaps and challenges for the community to consider in future research endeavours.
Smart contracts are technically defined as an event-driven programs, with state, that run on a distributed, decentralized, shared and replicated ledger (blockchain) and that can take custody over and transfer assets on the ledger. This new invention enables declarations of will to be expressed as self-executing computer code. The fact that smart contracts can transfer assets without the need for judicial system creates many questions about their place in the civil law. Also, it raises a question about their legality. This paper explores some basic concepts related to smart contracts and tries to set boundaries to their legality in the framework of civil law in scope of form and interpretation.
Polina Mamoshina, Lucy O. Ojomoko, Yury Yanovich, Alex Ostrovski · 12 authors
// Polina Mamoshina 1,2 , Lucy Ojomoko 1 , Yury Yanovich 3 , Alex Ostrovski 3 , Alex Botezatu 3 , Pavel Prikhodko 3 , Eugene Izumchenko 4 , Alexander Aliper 1 , Konstantin Romantsov 1 , Alexander Zhebrak 1 , Iraneus Obioma Ogu 5 and Alex Zhavoronkov 1,6 1 Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern, Baltimore, Maryland, USA 2 Department of Computer Science, University of Oxford, Oxford, United Kingdom 3 The Bitfury Group, Amsterdam, Netherlands 4 Department of Otolaryngology-Head & Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA 5 Africa Blockchain Artificial Intelligence for Healthcare Initiative, Insilico Medicine, Inc, Abuja, Nigeria 6 The Biogerontology Research Foundation, London, United Kingdom Correspondence to: Alex Zhavoronkov, email: // Keywords : artificial intelligence; deep learning; data management; blockchain; digital health Received : October 19, 2017 Accepted : November 02, 2017 Published : November 09, 2017 Abstract The increased availability of data and recent advancements in artificial intelligence present the unprecedented opportunities in healthcare and major challenges for the patients, developers, providers and regulators. The novel deep learning and transfer learning techniques are turning any data about the person into medical data transforming simple facial pictures and videos into powerful sources of data for predictive analytics. Presently, the patients do not have control over the access privileges to their medical records and remain unaware of the true value of the data they have. In this paper, we provide an overview of the next-generation artificial intelligence and blockchain technologies and present innovative solutions that may be used to accelerate the biomedical research and enable patients with new tools to control and profit from their personal data as well with the incentives to undergo constant health monitoring. We introduce new concepts to appraise and evaluate personal records, including the combination-, time- and relationship-value of the data. We also present a roadmap for a blockchain-enabled decentralized personal health data ecosystem to enable novel approaches for drug discovery, biomarker development, and preventative healthcare. A secure and transparent distributed personal data marketplace utilizing blockchain and deep learning technologies may be able to resolve the challenges faced by the regulators and return the control over personal data including medical records back to the individuals.
Open access
Blockchain Technology Applications and Security
Artificial Intelligence in Healthcare and Education
Blockchain has recently been applied in many applications such as bitcoin, smart grid, and Internet of Things (IoT) as a public ledger of transactions. However, the use of blockchain in mobile environments is still limited because the mining process consumes too much computing and energy resources on mobile devices. Edge computing offered by the Edge Computing Service Provider can be adopted as a viable solution for offloading the mining tasks from the mobile devices, i.e., miners, in the mobile blockchain environment. However, a mechanism needs to be designed for edge resource allocation to maximize the revenue for the Edge Computing Service Provider and to ensure incentive compatibility and individual rationality is still open. In this paper, we develop an optimal auction based on deep learning for the edge resource allocation. Specifically, we construct a multi-layer neural network architecture based on an analytical solution of the optimal auction. The neural networks first perform monotone transformations of the miners' bids. Then, they calculate allocation and conditional payment rules for the miners. We use valuations of the miners as the data training to adjust parameters of the neural networks so as to optimize the loss function which is the expected, negated revenue of the Edge Computing Service Provider. We show the experimental results to confirm the benefits of using the deep learning for deriving the optimal auction for mobile blockchain with high revenue
Abstract Could it be right to convict and punish defendants using only statistical evidence? In this paper, I argue that it is not and explain why it would be wrong. This is difficult to do because there is a powerful argument for thinking that we should convict and punish defendants using statistical evidence. It looks as if the relevant cases are cases of decision under risk and it seems we know what we should do in such cases (i.e., maximize expected value). Given some standard assumptions about the values at stake, the case for convicting and punishing using statistical evidence seems solid. In trying to show where this argument goes wrong, I shall argue (against Lockeans, reliabilists, and others) that beliefs supported only by statistical evidence are epistemically defective and (against Enoch, Fisher, and Spectre) that these epistemic considerations should matter to the law. To solve the puzzle about the role of statistical evidence in the law, we need to revise some commonly held assumptions about epistemic value and defend the relevance of epistemology to this practical question.
Felix Engelmann, Florian Glaser, Henning Kopp, Frank Kargl · 5 authors
Payment channel networks are supposed to overcome technical scalability limitations of blockchain infrastructure by employing a special overlay network with fast payment confirmation and only sporadic settlement of netted transactions on the blockchain. However, they introduce economic routing constraints that limit decentralized scalability and are currently not well understood. In this paper, we model the economic incentives for participants in payment channel networks. We provide the first formal model of payment channel economics and analyze how the cheapest path can be found. Additionally, our simulation assesses the long-term evolution of a payment channel network. We find that even for small routing fees, sometimes it is cheaper to settle the transaction directly on the blockchain.
Public key quantum money can be seen as a version of the quantum no-cloning theorem that holds even when the quantum states can be verified by the adversary. In this work, investigate quantum lightning, a formalization of "collision-free quantum money" defined by Lutomirski et al. [ICS'10], where no-cloning holds even when the adversary herself generates the quantum state to be cloned. We then study quantum money and quantum lightning, showing the following results: - We demonstrate the usefulness of quantum lightning by showing several potential applications, such as generating random strings with a proof of entropy, to completely decentralized cryptocurrency without a block-chain, where transactions is instant and local. - We give win-win results for quantum money/lightning, showing that either signatures/hash functions/commitment schemes meet very strong recently proposed notions of security, or they yield quantum money or lightning. - We construct quantum lightning under the assumed multi-collision resistance of random degree-2 systems of polynomials. - We show that instantiating the quantum money scheme of Aaronson and Christiano [STOC'12] with indistinguishability obfuscation that is secure against quantum computers yields a secure quantum money scheme
A number of information and resource sharing systems arise and become popular with the rapid development of communication technologies and mobile smart devices. The interactions between humans and machines are intense and their synergistic reactions have attracted special attention for the reason of forming so called Human–Machine Networks (HMN). HMNs refer to these networks where humans and machines work together to provide synergistic effects on their payoffs. Game theory, which can capture the interactions among players dexterously, has been widely used in solving various problems in HMN systems from the view of economics. In this paper, we extensively review the literature about game theoretical methods in HMNs, in particular focusing on its typical systems such as crowdsourcing, an elemental HMN and Internet of Things (IoT), a hybrid HMN, as well as Bitcoin. We propose a series of requirements to evaluate existing work. For reviewing and analyzing each system, we specify application purposes, players, strategies, game models and equilibria based on our proposed requirements. In the sequel, we identify a number of common and distinct open issues in HMNs and point out future research directions.
A blockchain is a distributed transaction ledger, a disruptive technology that creates new possibilities for digital ecosystems. The blockchain ecosystem maintains an immutable transaction record to support many types of digital services. This paper compares the performance and scalability of a web-based groupware communication application using both non-blockchain and blockchain technologies. Scalability is measured where message load is synthesized over two typical communication topologies. The first is 1 to n network -- a typical client-server or star-topology with a central vertex (server) receiving all messages from the remaining n - 1 vertices (clients). The second is a more naturally occurring scale-free network topology, where multiple communication hubs are distributed throughout the network. System performance is tested with both blockchain and non-blockchain solutions using multiple cloud computing configurations. We analyze the empirical results from each configuration to identify the costs and overhead of blockchain technology.
Five billion people in the world do not have the kind of ledgers that provide the documented information that allows them to transfer, partition, and aggregate assets and talents in such a way that they can be scaled up, secure investment, guarantee credit, certify reputation, and capture abstract surplus value. The difficulty that most people have in making combinations is a major cause of global inequality and unnecessary poverty.
In this paper, we propose a zero-knowledge proof for a special case of the hidden subset sum problem. This problem was presented by [Boyko et al. 1998] as the underlying problem of methods for generating random pairs of the form (x, gx (mod p)) using precomputations. The proof we propose is an adaptation of a zero-knowledge protocol for the subset sum problem presented by [Blocki 2009].
Antonio Fernández Anta, Chryssis Georgiou, Nicolas Nicolaou
In his PODC’2017 keynote address, Maurice Herlihy pointed out that despite the hype about blockchains and distributed ledgers, no formal abstraction of these objects has been proposed. To face this issue, in this paper we provide a proper formulation of a distributed ledger object. In brief, we define a ledger object as a sequence of records, and we provide the operations and the properties that such an object should support. We then provide a variation of the ledger – the validated ledger – which requires that each record in the ledger satisfies a particular validation rule. A (validated) ledger is distributed if it is implemented on top of multiple (possibly geographically dispersed) computing devices.
Saurabh Raje, Shyamal Vaderia, Neil Wilson, Rudrakh Panigrahi
This paper describes the design and development of a decentralized firewall system powered by a novel malware detection engine. The firewall is built using blockchain technology. The detection engine aims to classify Portable Executable (PE) files as malicious or benign. File classification is carried out using a deep belief neural network (DBN) as the detection engine. Our approach is to model the files as grayscale images and use the DBN to classify those images into the aforementioned two classes. An extensive data set of 10,000 files is used to train the DBN. Validation is carried out using 4,000 files previously unexposed to the network. The final result of whether to allow or block a file is obtained by arriving at a proof of work based consensus in the blockchain network.
As the core issue of blockchain, the mining requires solving a proof-of-work puzzle, which is resource expensive to implement in mobile devices due to the high computing power needed. Thus, the development of blockchain in mobile applications is restricted. In this paper, we, for the first time, consider the edge computing as the network enabler for mobile blockchain. In particular, we study optimal pricing-based edge computing resource management to support mobile blockchain applications where the mining process can be offloaded to an Edge computing Service Provider (ESP). We adopt a two-stage Stackelberg game to jointly maximize the profit of the ESP and the individual utilities of different miners. In Stage~I, the ESP sets the price of edge computing services. In Stage~II, the miners decide on the service demand to purchase based on the observed prices. We apply the backward induction to analyze the sub-game perfect equilibrium in each stage for uniform and discriminatory pricing schemes. Further, the existence and uniqueness of Stackelberg game are validated for both pricing schemes. At last, the performance evaluation shows that the ESP intends to set the maximum possible value as optimal price for profit maximization under uniform pricing. In addition, the discriminatory pricing helps the ESP to encourage higher total service demand from miners and achieve greater profit correspondingly.