After comparing and contrasting with computer codes running in a central server, this paper notes that smart contracts are not in the legal sense and considers their implications for contract management and dispute prevention. It alerts that the features of are prone to generate disputes which often involve novel legal issues. The paper concludes with a brief comment on the potential use of in dispute resolution.
The cryptocurrency Bitcoin has been prominently featured in the news recently. Its ascension in value has been nothing short of extraordinary. This article briefly explains what Bitcoin is and how it works. The more challenging question is what Bitcoin—this cryptographic breakthrough—really is: currency, like the U.S. dollar, an asset, more like gold, or something else? Further, can this astonishing run-up in value continue or will Bitcoin be added to the long list of so-called "asset bubbles" that eventually burst, causing pain for those that own them?
We design and implement the first private and anonymous decentralized crowdsourcing system ZebraLancer, and overcome two fundamental challenges of decentralizing crowdsourcing, i.e., data leakage and identity breach. First, our outsource-then-prove methodology resolves the tension between the blockchain transparency and the data confidentiality to guarantee the basic utilities/fairness requirements of data crowdsourcing, thus ensuring: (i) a requester will not pay more than what data deserve, according to a policy announced when her task is published via the blockchain; (ii) each worker indeed gets a payment based on the policy, if he submits data to the blockchain; (iii) the above properties are realized not only without a central arbiter, but also without leaking the data to the open blockchain. Second, the transparency of blockchain allows one to infer private information about workers and requesters through their participation history. Simply enabling anonymity is seemingly attempting but will allow malicious workers to submit multiple times to reap rewards. ZebraLancer also overcomes this problem by allowing anonymous requests/submissions without sacrificing accountability. The idea behind is a subtle linkability: if a worker submits twice to a task, anyone can link the submissions, or else he stays anonymous and unlinkable across tasks. To realize this delicate linkability, we put forward a novel cryptographic concept, i.e., the common-prefix-linkable anonymous authentication. We remark the new anonymous authentication scheme might be of independent interest. Finally, we implement our protocol for a common image annotation task and deploy it in a test net of Ethereum. The experiment results show the applicability of our protocol atop the existing real-world blockchain.
Robocalling has become an increasing problem in the Public Switched
Telephone Network (PSTN). While techniques like verified caller ID can
help reduce its impact, ultimately robocalling will continue until
economically it is no longer viable. This document proposes a new type
of cryptocurrency, called SIPCoin, which is used to create a tax - in
the form of computation - that must be paid before placing an inter-
domain call on the SIP-based public telephone network. SIPCoin
maintains complete anonymity of calls, is non-transferable between
users avoiding its usage as an exchangeable currency, causes minimal
increase call setup delays, and makes use of traditional certificate
authority trust chains to validate proofs of work. SIPCoin is best
used in concert with whitelist based techniques to minimize costs on
known valid callers.
This article applies ‘outcome-driven innovation’ methodology, developed by Anthony Ulwick and popularised by Clayton Christensen, to the domain of clinical trials. Data were collected through in-depth, open interviews with doctors, nurses and researchers in the UK, France and Italy. Pain points for key players of the value chain were identified. The findings supported a multi-stakeholder approach to fully exploit the transformative potential of blockchain technology to resolve issues. The research identified a set of opportunities for innovators to improve the way hospitals conduct clinical trials using smart contracts and blockchain technology generally.
By thinking loudly about putting the regulation of cryptocurrencies on the agenda of the G20, governments seem to have managed to keep the Bitcoin bubble from inflating into a systemic risk, so far. In a tongue-in-cheek sense, this behavior of supervisory and regulatory authorities can be described as the distributed ledger technology of financial supervision. It is distributed because it does not have a clear center. The G20 seems to be the common reference point for many actors, but it does not speak itself. It is like a shared code.
Blockchains have recently generated explosive interest from both academia and industry, with many proposed applications. But descriptions of many these proposals are more visionary projections than realizable proposals, and even basic definitions are often missing. We define "blockchain" and "blockchain network", and then discuss two very different, well known classes of blockchain networks: cryptocurrencies and Git repositories. We identify common primitive elements of both and use them to construct a framework for explicitly articulating what characterizes blockchain networks. The framework consists of a set of questions that every blockchain initiative should address at the very outset. It is intended to help one decide whether or not blockchain is an appropriate approach to a particular application, and if it is, to assist in its initial design stage.
Soon after its introduction in 2009, Bitcoin has been adopted by cyber-criminals, which rely on its pseudonymity to implement virtually untraceable scams. One of the typical scams that operate on Bitcoin are the so-called Ponzi schemes. These are fraudulent investments which repay users with the funds invested by new users that join the scheme, and implode when it is no longer possible to find new investments. Despite being illegal in many countries, Ponzi schemes are now proliferating on Bitcoin, and they keep alluring new victims, who are plundered of millions of dollars. We apply data mining techniques to detect Bitcoin addresses related to Ponzi schemes. Our starting point is a dataset of features of real-world Ponzi schemes, that we construct by analysing, on the Bitcoin blockchain, the transactions used to perform the scams. We use this dataset to experiment with various machine learning algorithms, and we assess their effectiveness through standard validation protocols and performance metrics. The best of the classifiers we have experimented can identify most of the Ponzi schemes in the dataset, with a low number of false positives.
Blockchain, as secure means for asset transfer, greatly attracts the attention of the global economic community. However, scalability strongly hampers the growth of economic systems based on the blockchain technology. Bidirectional micropayment channels are suitable solutions for scalability on economic systems based on blockchain. However, the challenge presented by micropayment channels is the inefficient routing mechanisms and timelocks generated per channel for a given path chosen to execute a transaction. In this paper, we develop suitable routing algorithms for economic systems based on blockchain. We additionally derive suitable equations to generate unique timelocks for channels belonging to selected paths chosen to execute a transaction. Finally, we demonstrate the efficiency of the protocol based on number of connections and bandwidth with evidence showing the efficiency and scalability of the routing mechanism.
With the development of marine observation technology and network technology, the volume of marine data growing rapidly. This brings new challenges for data storage and transmission. How to protect data security of marine big data has become an urgent problem. The traditional information security methods' characteristic is centralization. These technologies cannot provide whole process protection, e.g., data storage, data management and application of data. The blockchain technology is a novel technology, which can keep the data security and reliability by using decentralized methodology. It has aroused wide interest in the financial field. In this paper, we describe the concept, characteristics and key technologies of blockchain technology and introduce it into the field of marine data security.
The same investors who invest in normal markets, using the same strategies they use in normal markets, for the same motivations they have in normal markets, can cause bubbles.
Cryptocurrency is a form of secured digital asset. Cryptocurrency is regulated without interference of any centralized financial authorities like Banks. It is controlled online by a group of currency holders themselves which is possible only using Internet. Cryptocurrencies use decentralized technology to let its users do secured transactions and keep record of all transactions without the need to use intermediate Financial Institutes like bank. They are maintained on a distributed open catalog called blockchain, which is a record of all transactions updated and held by currency holders.
The network-centric world of the 21st century and explosive growth of the internet related technologies brought modern cybersecurity culture with complex threat landscape in the Higher Education environment. Cybersecurity landscape is always changing and education providers often do not have remit or in fact the means and capacity to cover the range of activities learners engage with, which attest their achievements, knowledge, and skills. Currently the awarding and validation of qualifications occurs exclusively under centralised management of an education institution or an employer take more ownership of the learning experience and its outcomes without compromising on safety, security, and accessibility. The centralised model of the present awarding and validation is no longer sustainable because learning happens increasingly on online platforms, and learning is far more international than it used to be. Key higher educational providers introduced degree apprenticeships which are new way to ‘do both’ higher level skills and provide progression routes to improve their employability prospects. \n \nThe ‘Blockchain’ (BC) facilitates digitized, decentralized, public ledger of all cryptocurrency transactions. It embraces a set of inter-related technologies. This paper expounds a novel BC-based architecture for transform centralised model of awarding and validation in to decentralized ledger of secured database. This database is shared, replicated, and synchronized for validation among the universities, partner institutions, professionals, statutory or regulatory bodies and industry bodies across the internet. The architecture offers secured collaborative validating system by qualification exchange with BC using trust methods within the decentralized topology.
Although the problems identified in the statement have been known for several decades, previous expressions of concern and calls for action have not fostered broad improvements in practice.2 A P value of 0.05 carries a 5% risk of a false positive result (i.e. there is no true difference between treatments). If a trial is meant to provide proof of a genuine treatment difference beyond reasonable doubt, a much smaller P value – say p < 0. 001 – is required.5 We disagree ….that our statement… is erroneous. According to the null hypothesis, P < 0.05 will occur 5% of the time.6 No editorial corrigendum has appeared. A P-value is the area under the curve of a probability distribution defined by a mathematical model. The model, usually presented graphically, describes the expected distribution of a sample statistic around a central measure, the parameter or theoretical ‘true’ value, for example the population mean, μ. Under the central limit theorem, this would be the standard normal distribution of sample means generated by repeat sampling of a population variable of interest. The mean of the sample means would equal the ‘true’ population mean, μ. In medicine, it is rare for us ever to know the true value of the variable of interest. However, we can usefully assign a value in the special case of a difference statistic, for example the difference in mean outcome variables in a placebo-controlled drug trial. In this case, the sampling distribution would represent that of the difference statistic. In this case, if the value we assign μ is zero then the mathematical model becomes the null hypothesis used in NHST. By way of contrast, non-inferiority drug trials require a non-zero value to be assigned. The cumulative AUC of the sampling distribution of a continuous variable is represented by a mathematical function called the cumulative density function. In medical science, most study variables are continuous or, if categorical, are transformed using the logit model. As the P-value is a mathematical integral, that is the cumulative AUC, it cannot take on a precise value as there is no AUC defined by a single point on the curve, for example the P-value ≤ 0.05, but not P = 0.05. While this may seem pedantic, the semantics of statistical inference are influential in thinking and decision-making yet misinterpretation and misuse of terminology are commonplace. Under the null hypothesis, one sample mean that happens to fall within an extreme region of the standard normal distribution may be expected to occur with a low frequency, say P ≤ 0.05 meaning such a sample mean or one more extreme would be expected to occur with a frequency of 5% or less. To be valid, the assumptions of independence and random selection of each sample mean selected from the normal distribution of sample means must be assumed. Another way of stating this is as a conditional probability: . Note: | means ‘given’. It is important to understand that the P-value is a measure conditional on the assumption that the mathematical model describes the distribution of sample means and is not a measure of the probability of the ‘truth’ of the mathematical model. To make this claim would invert the conditional probability statement and commit an error of reasoning called transposing the conditional7 aka the prosecutor's fallacy: . In reasoning from NHST, the commonly used definition of the P-value as ‘a measure of evidence against the null hypothesis’ is potentially misleading in that it seems to legitimise transposing the conditional as if it were a mathematically valid function rather than a matter of intuition. It was the intuitive interpretation that Fisher used in his a posteriori model of NHST.8, 9 His aim was to use the P-value as an aid in deciding which experiments to repeat. If on several repetitions, a consistent extreme P-value for the sample statistic was obtained then that would accumulate evidence for a true experimental effect. If no such effect was present, regression to the mean parameter (μ) would be expected (P ≥ 0.05). In real-life scenarios, many factors inhibit repetition and replication of experiments; however, modelling can give us insight into the precision and reproducibility of extreme P-values10, 11 and hence the intuitive weight we place on the P-value ‘as a measure of evidence against the null hypothesis’. Table 2 is a reproduction.10 It describes the results of simulating repeat experimentation and the probability of producing a P-value ≤ 0.05 under the prescribed conditions of the simulated experiment. It may be surprising to many how poorly reproducible the P-value is as a bright line test (a bright line test is a clearly defined rule or standard, the purpose of which is to produce consistent and predictable results). For example, if in the first experiment P ≤ 0.05 was produced there would be a 50% probability of reproducing P ≤ 0.05 in a repeat experiment; if P ≤ 0.01was produced in the first experiment the probability of producing P ≤ 0.05 in a repeat experiment, would be 73%; and if P ≤ 0.001 was produced in the first experiment the probability of P ≤ 0.05 in a repeat experiment would be 91%. The magnitudes of a number of these first experiment P-values are those commonly used in pharmaceutical trials and other medical analyses. The P-value is also sensitive to sample size. Irrespective of the effect size, with increasing sample size (n) the P-value can be made as small as you wish12 because the standard error is proportional to the inverse of n. If statistical significance is substituted for ‘clinical significance’ even small irrelevant differences may be regarded as worthy of investment. Large sample sizes are often a feature of pharmaceutical trials of secondary and primary prevention interventions such as preventive therapies in atherosclerotic diseases and osteoporosis. The quoted extract from the article on clinical trials mistakenly promotes the P-value as a measure of error and further states that the error rate can legitimately be adjusted depending on the magnitude of the P-value thus providing ‘proof of a genuine treatment difference beyond reasonable doubt’. This erroneous interpretation has arisen from the illusion of coherence resulting from the conflation of the dominant models of hypothesis testing.8, 9 The setting of theoretical type 1 (α) and type 2 (β) error rates in the Neyman and Pearson model envisions the frequency of error ‘in the long run of experience’ (experimental repetition) given randomness and independence of sample means from two juxtaposed probability distributions. A priori two identical populations are imagined except that they differ in mean parameters, null μ0 and alternative μA. This model is valuable in providing a rationality to sample size selection. However, the conflation has resulted in confusion between Fisher's P-value and Neyman's α giving the P-value an apparent legitimacy as an a posteriori ‘sliding’ type 1 error rate. Even if this were logical, decreasing α would increase β, resulting in a decrease in power (1-β). Also the dichotomous approach of pitting null hypothesis against alternative hypothesis carries the risk of blinding the researcher or the consumer to other explanatory hypotheses. For those who think the use of confidence intervals (CI) overcomes the problems described, think again. Although it has greater intuitive value especially with respect to estimating effect size, the CI relies on the same premises as the P-value. For example the CI of juxtaposed probability distributions can be made as large or as small as can be paid for by increasing the sample size such that for any small difference the CI can be made not to overlap. Statistical analyses are very valuable tools for extracting information from data. However, the reliability of the knowledge generated is dependent on many more important factors inter alia, evidential justification of the experimental hypothesis, study design, study conduct and data collection and cleansing, competence in choice of statistical model, valid reasoning, reviewer bias, publication bias and replication. Much of the criticism of medical science centres on its overemphasis on the importance of the P-value, NHST and statistically defined effect sizes. A better understanding of how sound statistical inferences are made and how they influence decision making will be key elements to improving all aspects of healthcare. This is critically important in acknowledgement of individuals as complex adaptive systems with characteristics of emergence, adaptability, non-linearity and unpredictability13 rather than as static population averages. Surveys suggest statistical literacy amongst doctors is low.14, 15 Teaching and assessing knowledge and application of statistical inference, critical appraisal and decision-making skills should be a primary focus of medical schools and specialist colleges. Difficult concepts underpinning statistical inference may be more effectively and efficiently taught using computer simulation whereby the learner can manipulate effect sizes, sample sizes and other statistics in order to see how parameter estimates, P-values and CI change with reproduction and replication.16 This will foster a more in-depth understanding of the limits of statistical inference, making clinicians better able to choose wisely amongst the myriad of investigations and treatment options on offer. Subsequent to article submission and review the author attended the referenced ASA conference.2 A special issue of the ASA journal reporting the conference proceedings is planned for 2018. In the opening addresses, the 400 participants were encouraged to devote their energies to developing proposals and goals to address the long standing yet stubbornly persistent errors in statistical inference described in this article. While concrete proposals are yet to be endorsed by the ASA, many speakers emphasised the need to place greater emphasis on teaching the conceptual framework of the different philosophical approaches to science (mastering the concepts as a priority rather than the mechanics of statistical inference). The need for better understanding of statistical semantics on the part of non-statistician scientists was also highlighted. Further that the best way to achieve understanding would be to develop context-specific learning modules. An aspect of the conference that resonated with the author with respect to prediction in medical science was the idea that science defines degrees of uncertainty (not certainty) apropos caution must be applied to the use of prediction models in medical practice lest they be over-extended.
Danda B. Rawat, Laurent Njilla, Kevin Kwiat, Charles Kamhoua
In this paper, we design, develop, and evaluate a novel information sharing (iShare) framework for cybersecurity with the goal of protecting confidential information and networked infrastructures from future cyber-attacks. The proposed iShare framework leverages the Blockchain concept used in Bitcoin systems where multiple organizations/agencies participate for information sharing (without violating their privacy) to secure and monitor their cyberspace. Note that the Bitcoin for financial transactions has already demonstrated that there is a trusted, auditable sharing with peer-to-peer communications accompanied by a public ledger. The main aim of the Blockchain-based iShare framework is to constantly collect high-resolution, cyber-attack information across organizational boundaries of which the organizations have no specific knowledge or control over any other organizations' data or damage caused by cyber-attacks. In the proposed iShare framework, the decentralized nature of the Blockchain and digitally signed transactions ensure that an adversary cannot pose as a legitimate organization/user or cannot control/hamper the system because of the digital-signatures and cannot learn anything from the public ledger that has just hashed pointers. Moreover, we analyze the security attacks by outsiders (not participating in the iShare) using a Stackelberg game.
Recep Ahmet Saritekin, Eren Karabacak, Zubeyir Durgay, Enis Karaarslan
The convergence which brought with the globalizing world, highly affects the network and the communication sectors. Mostly closed source and centralized systems are used for network and communication. This contradicts with the information security principles and privacy. These centralized systems can not fully meet the concept of transparent, reliable, fast and uninterrupted communication. Distributed, decentralized and also transparent communication is possible with the blockchain technology. In this study, a communication application which is based on blockchain technology is proposed. InterPlanetary File System (IPFS) is preferred to overcome the limits of the blockchain. A prototype implementation called Cryptouch is proposed. Application features and potential benefits are discussed.