The Poverty of Linear Nations: Lessons from Taking an AK Model to the Data.

This paper takes an AK model to the PWT data. In the model, intratemporal and intertemporal shocks are reduced forms for different technologies, and determine the variation of the growth rate. Using the policy functions of the model we recover time series for the unobserved technology shock for a panel of countries. We can then evaluate both how well the model fits the data and what the contribution of the different shocks to the variation of growth rates is. We find that the data is largely inconsistent with the AK structure. However, we isolate what we believe are pervasive patterns in macroeconomic models: a negative correlation between intra and intertemporal shocks, and an ever increasing level of technology matched with ever cheaper consumption relative to investment.endogenous growth; technology shocks; investment shocks

Systemization of Pluggable Transports for Censorship Resistance

An increasing number of countries implement Internet censorship at different scales and for a variety of reasons. In particular, the link between the censored client and entry point to the uncensored network is a frequent target of censorship due to the ease with which a nation-state censor can control it. A number of censorship resistance systems have been developed thus far to help circumvent blocking on this link, which we refer to as link circumvention systems (LCs). The variety and profusion of attack vectors available to a censor has led to an arms race, leading to a dramatic speed of evolution of LCs. Despite their inherent complexity and the breadth of work in this area, there is no systematic way to evaluate link circumvention systems and compare them against each other. In this paper, we (i) sketch an attack model to comprehensively explore a censor's capabilities, (ii) present an abstract model of a LC, a system that helps a censored client communicate with a server over the Internet while resisting censorship, (iii) describe an evaluation stack that underscores a layered approach to evaluate LCs, and (iv) systemize and evaluate existing censorship resistance systems that provide link circumvention. We highlight open challenges in the evaluation and development of LCs and discuss possible mitigations.Comment: Content from this paper was published in Proceedings on Privacy Enhancing Technologies (PoPETS), Volume 2016, Issue 4 (July 2016) as "SoK: Making Sense of Censorship Resistance Systems" by Sheharbano Khattak, Tariq Elahi, Laurent Simon, Colleen M. Swanson, Steven J. Murdoch and Ian Goldberg (DOI 10.1515/popets-2016-0028

Early Warning Analysis for Social Diffusion Events

There is considerable interest in developing predictive capabilities for social diffusion processes, for instance to permit early identification of emerging contentious situations, rapid detection of disease outbreaks, or accurate forecasting of the ultimate reach of potentially viral ideas or behaviors. This paper proposes a new approach to this predictive analytics problem, in which analysis of meso-scale network dynamics is leveraged to generate useful predictions for complex social phenomena. We begin by deriving a stochastic hybrid dynamical systems (S-HDS) model for diffusion processes taking place over social networks with realistic topologies; this modeling approach is inspired by recent work in biology demonstrating that S-HDS offer a useful mathematical formalism with which to represent complex, multi-scale biological network dynamics. We then perform formal stochastic reachability analysis with this S-HDS model and conclude that the outcomes of social diffusion processes may depend crucially upon the way the early dynamics of the process interacts with the underlying network's community structure and core-periphery structure. This theoretical finding provides the foundations for developing a machine learning algorithm that enables accurate early warning analysis for social diffusion events. The utility of the warning algorithm, and the power of network-based predictive metrics, are demonstrated through an empirical investigation of the propagation of political memes over social media networks. Additionally, we illustrate the potential of the approach for security informatics applications through case studies involving early warning analysis of large-scale protests events and politically-motivated cyber attacks

Better insurance could effectively mitigate the increase in economic growth losses from U.S. hurricanes under global warming

Global warming is likely to increase the proportion of intense hurricanes in the North Atlantic. Here, we analyze how this may affect economic growth. To this end, we introduce an event-based macroeconomic growth model that temporally resolves how growth depends on the heterogeneity of hurricane shocks. For the United States, we find that economic growth losses scale superlinearly with shock heterogeneity. We explain this by a disproportional increase of indirect losses with the magnitude of direct damage, which can lead to an incomplete recovery of the economy between consecutive intense landfall events. On the basis of two different methods to estimate the future frequency increase of intense hurricanes, we project annual growth losses to increase between 10 and 146% in a 2°C world compared to the period 1980–2014. Our modeling suggests that higher insurance coverage can compensate for this climate change–induced increase in growth losses

Social push and the direction of innovation

Innovators are intrinsically-motivated individuals who use ideas to create new goods and services. This raises the possibility that their social backgrounds may affect the direction of their innovative activity. Consistent with this "social push" channel, we document that innovators create products that are more likely to be purchased by customers similar to them along observable dimensions including gender, age, and socioeconomic status, both across and within detailed industries. Next, we provide causal evidence that social experience affects the direction of a person's innovative activity. Specifically, being exposed to peers from a lower-income group increases an entrepreneur's propensity to create necessity products, without affecting her rates of entrepreneurship and entrepreneurial income. We incorporate this channel into a general equilibrium model to assess its implications for cost-of-living inequality and long-run growth when there is unequal access to the innovation system

Data-intensive innovation and the State: evidence from AI firms in China

Artificial intelligence (AI) innovation is data-intensive. States have historically collected large amounts of data, which is now being used by AI firms. Gathering comprehensive information on firms and government procurement contracts in China’s facial recognition AI industry, we first study how government data shapes AI innovation. We find evidence of a precise mechanism: because data is sharable across uses, economies of scope arise. Firms awarded public security AI contracts providing access to more government data produce more software for both government and commercial purposes. In a directed technical change model incorporating this mechanism, we then study the trade-offs presented by states’ AI procurement and data pro-vision policies. Surveillance states’ demand for AI may incidentally promote growth, but distort innovation, crowd-out resources, and infringe on civil liberties. Government data provision may be justified when economies of scope are strong and citizens’ privacy concerns are limited

TOWARDS RELIABLE CIRCUMVENTION OF INTERNET CENSORSHIP

The Internet plays a crucial role in today\u27s social and political movements by facilitating the free circulation of speech, information, and ideas; democracy and human rights throughout the world critically depend on preserving and bolstering the Internet\u27s openness. Consequently, repressive regimes, totalitarian governments, and corrupt corporations regulate, monitor, and restrict the access to the Internet, which is broadly known as Internet \emph{censorship}. Most countries are improving the internet infrastructures, as a result they can implement more advanced censoring techniques. Also with the advancements in the application of machine learning techniques for network traffic analysis have enabled the more sophisticated Internet censorship. In this thesis, We take a close look at the main pillars of internet censorship, we will introduce new defense and attacks in the internet censorship literature. Internet censorship techniques investigate users’ communications and they can decide to interrupt a connection to prevent a user from communicating with a specific entity. Traffic analysis is one of the main techniques used to infer information from internet communications. One of the major challenges to traffic analysis mechanisms is scaling the techniques to today\u27s exploding volumes of network traffic, i.e., they impose high storage, communications, and computation overheads. We aim at addressing this scalability issue by introducing a new direction for traffic analysis, which we call \emph{compressive traffic analysis}. Moreover, we show that, unfortunately, traffic analysis attacks can be conducted on Anonymity systems with drastically higher accuracies than before by leveraging emerging learning mechanisms. We particularly design a system, called \deepcorr, that outperforms the state-of-the-art by significant margins in correlating network connections. \deepcorr leverages an advanced deep learning architecture to \emph{learn} a flow correlation function tailored to complex networks. Also to be able to analyze the weakness of such approaches we show that an adversary can defeat deep neural network based traffic analysis techniques by applying statistically undetectable \emph{adversarial perturbations} on the patterns of live network traffic. We also design techniques to circumvent internet censorship. Decoy routing is an emerging approach for censorship circumvention in which circumvention is implemented with help from a number of volunteer Internet autonomous systems, called decoy ASes. We propose a new architecture for decoy routing that, by design, is significantly stronger to rerouting attacks compared to \emph{all} previous designs. Unlike previous designs, our new architecture operates decoy routers only on the downstream traffic of the censored users; therefore we call it \emph{downstream-only} decoy routing. As we demonstrate through Internet-scale BGP simulations, downstream-only decoy routing offers significantly stronger resistance to rerouting attacks, which is intuitively because a (censoring) ISP has much less control on the downstream BGP routes of its traffic. Then, we propose to use game theoretic approaches to model the arms races between the censors and the censorship circumvention tools. This will allow us to analyze the effect of different parameters or censoring behaviors on the performance of censorship circumvention tools. We apply our methods on two fundamental problems in internet censorship. Finally, to bring our ideas to practice, we designed a new censorship circumvention tool called \name. \name aims at increasing the collateral damage of censorship by employing a ``mass\u27\u27 of normal Internet users, from both censored and uncensored areas, to serve as circumvention proxies

Proactive techniques for correct and predictable Internet routing

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2006.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 185-193).The Internet is composed of thousands of autonomous, competing networks that exchange reachability information using an interdomain routing protocol. Network operators must continually reconfigure the routing protocols to realize various economic and performance goals. Unfortunately, there is no systematic way to predict how the configuration will affect the behavior of the routing protocol or to determine whether the routing protocol will operate correctly at all. This dissertation develops techniques to reason about the dynamic behavior of Internet routing, based on static analysis of the router configurations, before the protocol ever runs on a live network. Interdomain routing offers each independent network tremendous flexibility in configuring the routing protocols to accomplish various economic and performance tasks. Routing configurations are complex, and writing them is similar to writing a distributed program; the (unavoidable) consequence of configuration complexity is the potential for incorrect and unpredictable behavior. These mistakes and unintended interactions lead to routing faults, which disrupt end-to-end connectivity. Network operators writing configurations make mistakes; they may also specify policies that interact in unexpected ways with policies in other networks.(cont.) To avoid disrupting network connectivity and degrading performance, operators would benefit from being able to determine the effects of configuration changes before deploying them on a live network; unfortunately, the status quo provides them no opportunity to do so. This dissertation develops the techniques to achieve this goal of proactively ensuring correct and predictable Internet routing. The first challenge in guaranteeing correct and predictable behavior from a routing protocol is defining a specification for correct behavior. We identify three important aspects of correctness-path visibility, route validity, and safety-and develop proactive techniques for guaranteeing that these properties hold. Path visibility states that the protocol disseminates information about paths in the topology; route validity says that this information actually corresponds to those paths; safety says that the protocol ultimately converges to a stable outcome, implying that routing updates actually correspond to topological changes. Armed with this correctness specification, we tackle the second challenge: analyzing routing protocol configurations that may be distributed across hundreds of routers.(cont.) We develop techniques to check whether a routing protocol satisfies the correctness specification within a single independently operated network. We find that much of the specification can be checked with static configuration analysis alone. We present examples of real-world routing faults and propose a systematic framework to classify, detect, correct, and prevent them. We describe the design and implementation of rcc ("router configuration checker"), a tool that uses static configuration analysis to enable network operators to debug configurations before deploying them in an operational network. We have used rcc to detect faults in 17 different networks, including several nationwide Internet service providers (ISPs). To date, rcc has been downloaded by over seventy network operators. A critical aspect of guaranteeing correct and predictable Internet routing is ensuring that the interactions of the configurations across multiple networks do not violate the correctness specification. Guaranteeing safety is challenging because each network sets its policies independently, and these policies may conflict. Using a formal model of today's Internet routing protocol, we derive conditions to guarantee that unintended policy interactions will never cause the routing protocol to oscillate.(cont.) This dissertation also takes steps to make Internet routing more predictable. We present algorithms that help network operators predict how a set of distributed router configurations within a single network will affect the flow of traffic through that network. We describe a tool based on these algorithms that exploits the unique characteristics of routing data to reduce computational overhead. Using data from a large ISP, we show that this tool correctly computes BGP routing decisions and has a running time that is acceptable for many tasks, such as traffic engineering and capacity planning.by Nicholas Greer Feamster.Ph.D

On the cyber security issues of the internet infrastructure

The Internet network has received huge attentions by the research community. At a first glance, the network optimization and scalability issues dominate the efforts of researchers and vendors. Many results have been obtained in the last decades: the Internet’s architecture is optimized to be cheap, robust and ubiquitous. In contrast, such a network has never been perfectly secure. During all its evolution, the security threats of the Internet persist as a transversal and endless topic. Nowadays, the Internet network hosts a multitude of mission critical activities. The electronic voting systems and financial services are carried out through it. Governmental institutions, financial and business organizations depend on the performance and the security of the Internet. This role confers to the Internet network a critical characterization. At the same time, the Internet network is a vector of malicious activities, like Denial of Service attacks; many reports of attacks can be found in both academic outcomes and daily news. In order to mitigate this wide range of issues, many research efforts have been carried out in the past decades; unfortunately, the complex architecture and the scale of the Internet make hard the evaluation and the adoption of such proposals. In order to improve the security of the Internet, the research community can benefit from sharing real network data. Unfortunately, privacy and security concerns inhibit the release of these data: its suffices to imagine the big amount of private information (e.g., political preferences or religious belief) it is possible to get while reading the Internet packets exchanged between users and web services. This scenario motivates my research, and represents the context of this dissertation which contributes to the analysis of the security issues of the Internet infrastructures and describes relevant security proposals. In particular, the main outcomes described in this dissertation are: • the definition of a secure routing protocol for the Internet network able to provide cryptographic guarantees against false route announcement and invalid path attack; • the definition of a new obfuscation technique that allow the research community to publicly release their real network flows with formal guarantees of security and privacy; • the evidence of a new kind of leakage of sensitive informations obtained hacking the models used by sundry Machine Learning Algorithms

Dynamic Interaction and Manipulation of Web Resources

In this thesis we join methods for evaluating queries over interlinked resources via link traversal with approaches for the integration of data over interlinked schemata via reasoning. Our approach allows for the on-the-fly alignment and processing of dynamically retrieved data in a streaming fashion including incremental query answering. We go beyond the simple consumption of exposed information by enabling manipulations of remote resources in a parallel execution system