Cryptographic Software Export Controls in the EU

Certain software products employing digital techniques for encryption of data are subject to export controls in the EU Member States pursuant to Community law and relevant laws in the Member States. These controls are agreed globally in the framework of the so-called Wassenaar Arrangement. Wassenaar is an informal non-proliferation regime aimed at promoting international stability and responsibility in transfers of strategic (dual-use) products and technology. This thesis covers provisions of Wassenaar, Community export control laws and export control laws of Finland, Sweden, Germany, France and United Kingdom. This thesis consists of five chapters. The first chapter discusses the ratio of export control laws and the impact they have on global trade. The ratio is originally defence-related - in general to prevent potential adversaries of participating States from having the same tools, and in particular in the case of cryptographic software to enable signals intelligence efforts. Increasingly as the use of cryptography in a civilian context has mushroomed, export restrictions can have negative effects on civilian trade. Information security solutions may also be took weak because of export restrictions on cryptography. The second chapter covers the OECD's Cryptography Policy, which had a significant effect on its member nations' national cryptography policies and legislation. The OECD is a significant organization,because it acts as a meeting forum for most important industrialized nations. The third chapter covers the Wassenaar Arrangement. The Arrangement is covered from the viewpoint of international law and politics. The Wassenaar control list provisions affecting cryptographic software transfers are also covered in detail. Control lists in the EU and in Member States are usually directly copied from Wassenaar control lists. Controls agreed in its framework set only a minimum level for participating States. However, Wassenaar countries can adopt stricter controls. The fourth chapter covers Community export control law. Export controls are viewed in Community law as falling within the domain of Common Commercial Policy pursuant to Article 133 of the EC Treaty. Therefore the Community has exclusive competence in export matters, save where a national measure is authorized by the Community or falls under foreign or security policy derogations established in Community law. The Member States still have a considerable amount of power in the domain of Common Foreign and Security Policy. They are able to maintain national export controls because export control laws are not fully harmonized. This can also have possible detrimental effects on the functioning of internal market and common export policies. In 1995 the EU adopted Dual-Use Regulation 3381/94/EC, which sets common rules for exports in Member States. Provisions of this regulation receive detailed coverage in this chapter. The fifth chapter covers national legislation and export authorization practices in five different Member States - in Finland, Sweden, Germany, France and in United Kingdom. Export control laws of those Member States are covered when the national laws differ from the uniform approach of the Community's acquis communautaire

Distributed Bayesian Matrix Factorization with Limited Communication

Bayesian matrix factorization (BMF) is a powerful tool for producing low-rank representations of matrices and for predicting missing values and providing confidence intervals. Scaling up the posterior inference for massive-scale matrices is challenging and requires distributing both data and computation over many workers, making communication the main computational bottleneck. Embarrassingly parallel inference would remove the communication needed, by using completely independent computations on different data subsets, but it suffers from the inherent unidentifiability of BMF solutions. We introduce a hierarchical decomposition of the joint posterior distribution, which couples the subset inferences, allowing for embarrassingly parallel computations in a sequence of at most three stages. Using an efficient approximate implementation, we show improvements empirically on both real and simulated data. Our distributed approach is able to achieve a speed-up of almost an order of magnitude over the full posterior, with a negligible effect on predictive accuracy. Our method outperforms state-of-the-art embarrassingly parallel MCMC methods in accuracy, and achieves results competitive to other available distributed and parallel implementations of BMF.Comment: 28 pages, 8 figures. The paper is published in Machine Learning journal. An implementation of the method is is available in SMURFF software on github (bmfpp branch): https://github.com/ExaScience/smurf

Algorithms for Exact Structure Discovery in Bayesian Networks

Bayesian networks are compact, flexible, and interpretable representations of a joint distribution. When the network structure is unknown but there are observational data at hand, one can try to learn the network structure. This is called structure discovery. This thesis contributes to two areas of structure discovery in Bayesian networks: space--time tradeoffs and learning ancestor relations. The fastest exact algorithms for structure discovery in Bayesian networks are based on dynamic programming and use excessive amounts of space. Motivated by the space usage, several schemes for trading space against time are presented. These schemes are presented in a general setting for a class of computational problems called permutation problems; structure discovery in Bayesian networks is seen as a challenging variant of the permutation problems. The main contribution in the area of the space--time tradeoffs is the partial order approach, in which the standard dynamic programming algorithm is extended to run over partial orders. In particular, a certain family of partial orders called parallel bucket orders is considered. A partial order scheme that provably yields an optimal space--time tradeoff within parallel bucket orders is presented. Also practical issues concerning parallel bucket orders are discussed. Learning ancestor relations, that is, directed paths between nodes, is motivated by the need for robust summaries of the network structures when there are unobserved nodes at work. Ancestor relations are nonmodular features and hence learning them is more difficult than modular features. A dynamic programming algorithm is presented for computing posterior probabilities of ancestor relations exactly. Empirical tests suggest that ancestor relations can be learned from observational data almost as accurately as arcs even in the presence of unobserved nodes.Algoritmeja Bayes-verkkojen rakenteen tarkkaan oppimiseen Bayes-verkot ovat todennäköisyysmalleja, joiden avulla voidaan kuvata muuttujien välisiä suhteita. Bayes-verkko koostuu kahdesta osasta: rakenteesta ja kuhunkin muuttujaan liittyvästä ehdollisesta todennäköisyysjakaumasta. Rakenteen puolestaan muodostaa muuttujien välisiä riippuvuuksia kuvaava suunnattu syklitön verkko. Kun tarkasteltavaa ilmiötä hyvin kuvaavaa Bayes-verkkoa ei tunneta ennalta, mutta ilmiöön liittyvistä muuttujista on kerätty havaintoaineistoa, voidaan sopivia algoritmeja käyttäen yrittää löytää verkkorakenne, joka sovittuu aineistoon mahdollisimman hyvin. Nopeimmat tarkat rakenteenoppimisalgoritmit perustuvat niin kutsuttuun dynaamiseen ohjelmointiin, eli ne pitävät välituloksia muistissa ja näin välttävät suorittamasta samoja laskuja useaan kertaan. Vaikka tällaiset menetelmät ovat suhteellisen nopeita, niiden haittapuolena on suuri muistinkäyttö, joka estää suurten verkkojen rakenteen oppimisen. Väitöskirjan alkuosa käsittelee rakenteenoppimisalgoritmeja, jotka tasapainottelevat ajan- ja muistinkäytön välillä. Kirjassa esitellään menetelmiä, joilla verkon rakenne voidaan oppia tehokkaasti käyttäen hyväksi kaikki käytössä oleva tila. Uusi menetelmä mahdollistaa entistä suurempien verkkojen rakenteen oppimisen. Edellä mainittu menetelmä yleistetään ratkaisemaan Bayes-verkkojen rakenteenoppimisen lisäksi myös niin kutsuttuja permutaatio-ongelmia, joista tunnetuin lienee kauppamatkustajan ongelma. Väitöskirjan loppuosa käsittelee muuttujien välisien esi-isäsuhteiden oppimista. Kyseiset suhteet ovat kiinnostavia, sillä ne antavat lisätietoa muuttujien sekä suorista että epäsuorista syy-seuraussuhteista. Väitöskirjassa esitetään algoritmi esi-isäsuhteiden todennäköisyyksien laskemiseen. Algoritmin toimintaa tutkitaan käytännössä ja todetaan, että esi-isäsuhteita pystytään oppimaan melko hyvin jopa silloin, kun useat havaitsemattomat muuttujat vaikuttavat aineiston muuttujiin

Havaintoja Keski-Eurooppaan tehdyltä opintomatkalta 14.6. -1.7.1980

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