Chainspace is a decentralized infrastructure, known as a distributed ledger, that supports user defined smart contracts and executes user-supplied transactions on their objects. The correct execution of smart contract transactions is verifiable by all. The system is scalable, by sharding state and the execution of transactions, and using S-BAC, a distributed commit protocol, to guarantee consistency. Chainspace is secure against subsets of nodes trying to compromise its integrity or availability properties through Byzantine Fault Tolerance (BFT), and extremely highauditability, non-repudiation and 'blockchain' techniques. Even when BFT fails, auditing mechanisms are in place to trace malicious participants. We present the design, rationale, and details of Chainspace; we argue through evaluating an implementation of the system about its scaling and other features; we illustrate a number of privacy-friendly smart contracts for smart metering, polling and banking and measure their performance.

Coconut is a novel selective disclosure credential scheme supporting distributed threshold issuance, public and private attributes, re-randomization, and multiple unlinkable selective attribute revelations. Coconut integrates with blockchains to ensure confidentiality, authenticity and availability even when a subset of credential issuing authorities are malicious or offline. We implement and evaluate a generic Coconut smart contract library for Chainspace and Ethereum; and present three applications related to anonymous payments, electronic petitions, and distribution of proxies for censorship resistance. Coconut uses short and computationally efficient credentials, and our evaluation shows that most Coconut cryptographic primitives take just a few milliseconds on average, with verification taking the longest time (10 milliseconds).request Alberto: @George, Describe how the CoCoNut authorities can also be Chainspace nodes (to make clear the potential of CoCoNut when deeply related to blockchains), since we actually built all of this to have credentials in smart contracts. V. EVALUATION A. Primitives evaluationThe signature scheme has been implemented in python using the two crypo libraries petlib [1] and bplib [2]. The bilinear pairing works over a curve, using OpenSSL as the arithmetic backend. We have released the code as an open-source project on GitHub. 3 . Table I shows the mean (µ) and standard deviation ( 2 ) of the execution of each procedure described in section section II. Each entry is the result of 10,000 measured on an Octa-core Dell desktop computer, 3.6GHz Intel Xeon. This table shows that signing is much faster than verifying signatures (about 15 times faster for the scheme working on clear messages, therefore set to 32 bytes (for SHA-2). The size of a signature is 132 bytes. The highest transaction size appears when the user ask a signature on a hidden message. This comes from the fact that the proof s associated with the message is approximately 318 bytes; the proof v is only 157 bytes.Alberto: Update the above B. System evaluation Alberto: @Bano, test system on AWS (n authority): client latency vs t -ask n signatures (and n blind signatures) and check the time it takes to hear back from t authorities. VI. COMPARISON WITH RELATED WORKSAlberto: discuss crypto related works Alberto: compare results (speed and size) with alternatives to see why it is cool stuff; not many scheme have actually been 3 https://github.com/asonnino/coconut plies that the adversary needs to corrupt at least t authorities for this attack to happen. This property also prevents a single authority from taking the user money and disappear without issuing any token. À Ã Õ OE oe --"

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This paper aims to shed light on alternative news media ecosystems that are believed to have influenced opinions and beliefs by false and/or biased news reporting during the 2016 US Presidential Elections. We examine a large, professionally curated list of 668 hyper-partisan websites and their corresponding Facebook pages, and identify key characteristics that mediate the traffic flow within this ecosystem. We uncover a pattern of new websites being established in the run up to the elections, and abandoned after. Such websites form an ecosystem, creating links from one website to another, and by 'liking' each others' Facebook pages. These practices are highly effective in directing user traffic internally within the ecosystem in a highly partisan manner, with right-leaning sites linking to and liking other right-leaning sites and similarly left-leaning sites linking to other sites on the left, thus forming a filter bubble amongst news producers similar to the filter bubble which has been widely observed among consumers of partisan news. Whereas there is activity along both left-and right-leaning sites, right-leaning sites are more evolved, accounting for a disproportionate number of abandoned websites and partisan internal links. We also examine demographic characteristics of consumers of hyper-partisan news and find that some of the more populous demographic groups in the US tend to be consumers of more right-leaning sites.

Internet-wide scanning depends on a notion of liveness: does a target IP address respond to a probe packet? However, the interpretation of such responses, or lack of them, is nuanced and depends on multiple factors, including: how we probed, how different protocols in the network stack interact, the presence of filtering policies near the target, and temporal churn in IP responsiveness. Although often neglected, these factors can significantly affect the results of active measurement studies. We develop a taxonomy of liveness which we employ to develop a method to perform concurrent IPv4 scans using ICMP, five TCP-based, and two UDP-based protocols, comprehensively capturing all responses to our probes, including negative and cross-layer responses. Leveraging our methodology, we present a systematic analysis of liveness and how it manifests in active scanning campaigns, yielding practical insights and methodological improvements for the design and the execution of active Internet measurement studies.