The Boeing-Airbus “Can of Stink.” EUMA Papers, Vol. 5, No. 2 January 2008

The European Union and the United States have the largest trading relationship in the world, comprising about 60% of world trade (Perdikis and Read 267). They are also the greatest proponents of trade liberalization and multilateral trade cooperating in the world. Boeing was the undisputed king of aircraft manufacturing for most of the 20th century. Airbus has been encroaching upon that position since the 1970s. In 2000, Airbus dethroned Boeing in terms of sales, and the two have been in a bitter battle since. A subsidy dispute currently underway between Boeing (supported by the US) and Airbus (supported by the EU – mostly from Germany, England, France and Spain) could have devastating consequences upon that relationship and their credibility in promoting neoliberal values abroad. Most trade disputes are solved through consultation, without the need for a ruling by the World Trade Organization. Unlike most others, dispute regarding aircraft has elicited a special and unprecedented aggression on the part of these two trading partners

Critical transaction costs and 1-step asymptotic arbitrage in fractional binary markets

We study the arbitrage opportunities in the presence of transaction costs in a sequence of binary markets approximating the fractional Black-Scholes model. This approximating sequence was constructed by Sottinen and named fractional binary markets. Since, in the frictionless case, these markets admit arbitrage, we aim to determine the size of the transaction costs needed to eliminate the arbitrage from these models. To gain more insight, we first consider only 1-step trading strategies and we prove that arbitrage opportunities appear when the transaction costs are of order $o(1/\sqrt{N})$. Next, we characterize the asymptotic behavior of the smallest transaction costs $\lambda_c^{(N)}$, called "critical" transaction costs, starting from which the arbitrage disappears. Since the fractional Black-Scholes model is arbitrage-free under arbitrarily small transaction costs, one could expect that $\lambda_c^{(N)}$ converges to zero. However, the true behavior of $\lambda_c^{(N)}$ is opposed to this intuition. More precisely, we show, with the help of a new family of trading strategies, that $\lambda_c^{(N)}$ converges to one. We explain this apparent contradiction and conclude that it is appropriate to see the fractional binary markets as a large financial market and to study its asymptotic arbitrage opportunities. Finally, we construct a $1$-step asymptotic arbitrage in this large market when the transaction costs are of order $o(1/N^H)$, whereas for constant transaction costs, we prove that no such opportunity exists.Comment: 21 page

The Flow Fingerprinting Game

Linking two network flows that have the same source is essential in intrusion detection or in tracing anonymous connections. To improve the performance of this process, the flow can be modified (fingerprinted) to make it more distinguishable. However, an adversary located in the middle can modify the flow to impair the correlation by delaying the packets or introducing dummy traffic. We introduce a game-theoretic framework for this problem, that is used to derive the Nash Equilibrium. As obtaining the optimal adversary delays distribution is intractable, some approximations are done. We study the concrete example where these delays follow a truncated Gaussian distribution. We also compare the optimal strategies with other fingerprinting schemes. The results are useful for understanding the limits of flow correlation based on packet timings under an active attacker.Comment: Workshop on Information Forensics and Securit

Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning

Deep Learning has recently become hugely popular in machine learning, providing significant improvements in classification accuracy in the presence of highly-structured and large databases. Researchers have also considered privacy implications of deep learning. Models are typically trained in a centralized manner with all the data being processed by the same training algorithm. If the data is a collection of users' private data, including habits, personal pictures, geographical positions, interests, and more, the centralized server will have access to sensitive information that could potentially be mishandled. To tackle this problem, collaborative deep learning models have recently been proposed where parties locally train their deep learning structures and only share a subset of the parameters in the attempt to keep their respective training sets private. Parameters can also be obfuscated via differential privacy (DP) to make information extraction even more challenging, as proposed by Shokri and Shmatikov at CCS'15. Unfortunately, we show that any privacy-preserving collaborative deep learning is susceptible to a powerful attack that we devise in this paper. In particular, we show that a distributed, federated, or decentralized deep learning approach is fundamentally broken and does not protect the training sets of honest participants. The attack we developed exploits the real-time nature of the learning process that allows the adversary to train a Generative Adversarial Network (GAN) that generates prototypical samples of the targeted training set that was meant to be private (the samples generated by the GAN are intended to come from the same distribution as the training data). Interestingly, we show that record-level DP applied to the shared parameters of the model, as suggested in previous work, is ineffective (i.e., record-level DP is not designed to address our attack).Comment: ACM CCS'17, 16 pages, 18 figure

Asymptotic proportion of arbitrage points in fractional binary markets

A fractional binary market is an approximating sequence of binary models for the fractional Black-Scholes model, which Sottinen constructed by giving an analogue of the Donsker's theorem. In a binary market the arbitrage condition can be expressed as a condition on the nodes of a binary tree. We call "arbitrage points" the points in the binary tree which verify such an arbitrage condition and "arbitrage paths" the paths in the binary tree which cross at least one arbitrage point. Using this terminology, a binary market admits arbitrage if and only if there is at least one arbitrage point in the binary tree or equivalently if there is at least one arbitrage path. Following the lines of Sottinen, who showed that the arbitrage persists in the fractional binary market, we further prove that starting from any point in the tree, we can reach an arbitrage point. This implies that, in the limit, there is an infinite number of arbitrage points. Next, we provide an in-depth analysis of the asymptotic proportion of arbitrage points at asymptotic levels and of arbitrage paths in the fractional binary market. All these results are obtained by studying a rescaled disturbed random walk. We moreover show that, when $H$ is close to $1$, with probability $1$ a path in the binary tree crosses an infinite number of arbitrage points. In particular, for such $H$, the asymptotic proportion of arbitrage paths is equal to $1$

Transitions into permanent employment in Spain : an empirical analysis for young workers

We analyze the Spanish temporary workers’ transitions into permanent employment and to what extent those who become unemployed are able to achieve a permanent job. Our focus is placed on the role of the individual’s sequence of temporary contracts on the probability of moving from temporary into permanent employment. We apply multiplespell duration techniques to a longitudinal dataset of temporary workers obtained from Social Security records for the period 1996-2003. We basically find that even though transitions into permanent employment increase with tenure, temporary jobs do not constitute stepping stones towards permanent employment, since the probability of obtaining a permanent job decreases with repeated temporary jobs. Results also show that individuals with high duration of unemployment flow into permanent work less frequently.

Last Days in Havana

A Fernando Perez Film: Last Days in Havana (Ultimos Dias en La Habana) From Cuba, a lover letter to a city-in-waiting and its hard-up dreamers. Opens September 15, 2017 Coral Gables Art Cinemahttps://digitalcommons.fiu.edu/cri_events/1372/thumbnail.jp