Centrally Banked Cryptocurrencies

Current cryptocurrencies, starting with Bitcoin, build a decentralized blockchain-based transaction ledger, maintained through proofs-of-work that also generate a monetary supply. Such decentralization has benefits, such as independence from national political control, but also significant limitations in terms of scalability and computational cost. We introduce RSCoin, a cryptocurrency framework in which central banks maintain complete control over the monetary supply, but rely on a distributed set of authorities, or mintettes, to prevent double-spending. While monetary policy is centralized, RSCoin still provides strong transparency and auditability guarantees. We demonstrate, both theoretically and experimentally, the benefits of a modest degree of centralization, such as the elimination of wasteful hashing and a scalable system for avoiding double-spending attacks.Comment: 15 pages, 4 figures, 2 tables in Proceedings of NDSS 201

Cryptocurrencies and Bitcoin: Charting the Research Landscape

This systematic literature review examines cryptocurrencies (CCs) and Bitcoin. Because cryptocurrency research has not gained much attention from Information Systems (IS) researchers and needs a more vivid discussion, this review summarizes the main concepts of 42 papers and aligns them to IS Research. Although, cryptocurrency research has not reached IS mainstream yet, there is massive potential for multifaceted research ranging from protocol development to designing alternative digital currency schemes. Cryptocurrencies entail a core digital artifact and present a rich phenomenon based on the intertwining of technological artifacts and social contexts. We argue that cryptocurrencies are an alternative payment method that may replace intermediaries with cryptographic methods and should be embedded in the research areas of SIGeBIZ and SIGSEC. At the end of this literature review, we discuss some open research gaps like new business models based on cryptocurrencies or the influence of culture on cryptocurrencies and Bitcoin

Model-based engineering of animated interactive systems for the interactive television environment

Les interfaces graphiques étaient la plupart du temps statiques, et représentaient une succession d'états logiciels les uns après les autres. Cependant, les transitions animées entre ces états statiques font partie intégrante des interfaces utilisateurs modernes, et leurs processus de design et d'implémentations constituent un défi pour les designers et les développeurs. Cette thèse propose un processus de conception de systèmes interactifs centré sur les animations, ainsi qu'une architecture pour la définition et l'implémentation d'animations au sein des interfaces graphiques. L'architecture met en avant une approche à deux niveaux pour définir une vue haut niveau d'une animation (avec un intérêt particulier pour les objets animés, leurs propriétés à être animé et la composition d'animations) ainsi qu'une vue bas niveau traitant des aspects détaillés des animations tels que les timings et les optimisations. Concernant les spécifications formelles de ces deux niveaux, nous utilisons une approche qui facilite les réseaux de Petri orientés objets pour la conception, l'implémentation et la validation d'interfaces utilisateurs animées en fournissant une description complète et non-ambiguë de l'ensemble de l'interface utilisateur, y compris les animations. Enfin, nous décrivons la mise en pratique du processus présenté, illustré par un cas d'étude d'un prototype haute-fidélité d'une interface utilisateur, pour le domaine de la télévision interactive. Ce processus conduira à une spécification formelle et détaillée du système interactif, et incluera des animations utilisant des réseaux de Petri orientés objet (conçus avec l'outil PetShop CASE).Graphical User Interfaces used to be mostly static, representing one software state after the other. However, animated transitions between these static states are an integral part in modern user interfaces and processes for both their design and implementation remain a challenge for designers and developers. This thesis proposes a process for designing interactive systems focusing on animations, along with an architecture for the definition and implementation of animation in user interfaces. The architecture proposes a two levels approach for defining a high-level view of an animation (focusing on animated objects, their properties to be animated and on the composition of animations) and a low-level one dealing with detailed aspects of animations such as timing and optimization. For the formal specification of these two levels, we are using an approach facilitating object-oriented Petri nets to support the design, implementation and validation of animated user interfaces by providing a complete and unambiguous description of the entire user interface including animations. Finally, we describe the application of the presented process exemplified by a case study for a high-fidelity prototype of a user interface for the interactive Television domain. This process will lead to a detailed formal specification of the interactive system, including animations using object-oriented Petri nets (designed with the PetShop CASE tool)

Interpretable privacy with optimizable utility

International audienceIn this position paper, we discuss the problem of specifying privacy requirements for machine learning based systems, in an inter-pretable yet operational way. Explaining privacy-improving technology is a challenging problem, especially when the goal is to construct a system which at the same time is interpretable and has a high performance. In order to address this challenge, we propose to specify privacy requirements as constraints, leaving several options for the concrete implementation of the system open, followed by a constraint optimization approach to achieve an efficient implementation also, next to the interpretable privacy guarantees

Programmeerimiskeeled turvalise ühisarvutuse rakenduste arendamiseks

Turvaline ühisarvutus on tehnoloogia, mis lubab mitmel sõltumatul osapoolel oma andmeid koos töödelda neis olevaid saladusi avalikustamata. Kui andmed on esitatud krüpteeritud kujul, tähendab see, et neid ei dekrüpteerita arvutuse käigus kordagi. Turvalise ühisarvutuse teoreetilised konstruktsioonid on teada olnud juba alates kaheksakümnendatest, kuid esimesed praktilised teostused ja rakendused, mis päris andmeid töötlesid, ilmusid alles natuke enam kui kümme aastat tagasi. Nüüdseks on turvalist ühisarvutust kasutatud mitmes praktilises rakenduses ning sellest on kujunenud oluline andmekaitsetehnoloogia. Turvalise ühisarvutuse rakenduste arendamine on keerukas. Vahendid, mis aitavad kaasa arendusprotsessile, on veel väga uued, ning raamistikud on sageli liiga aeglased praktiliste rakenduste jaoks. Rakendusi on endiselt võimelised arendama ainult krüptograafiaeksperdid. Käesoleva töö eesmärk on teha turvalise ühisarvutuse raamistikke paremaks ning muuta ühisarvutusrakenduste arendamist kergemaks. Väidame, et valdkon- naspetsiifiliste programmeerimiskeelte kasutamine võimaldab turvalise ühisarvu- tuse rakenduste ja raamistike ehitamist, mis on samaaegselt lihtsasti kasutatavad, hea jõudlusega, hooldatavad, usaldusväärsed ja võimelised suuri andmemahtusid töötlema. Peamise tulemusena esitleme kahte uut programmeerimiskeelt, mis on mõeldud turvalise ühisarvutuse jaoks. SecreC 2 on mõeldud turvalise ühisarvutuse rakendus- te arendamise lihtsustamiseks ja aitab kaasa sellele, et rakendused oleks turvalised ja efektiivsed. Teine keel on loodud turvalise ühisarvutuse protokollide arenda- miseks ning selle eesmärk on turvalise ühisarvutuse raamistikke paremaks muuta. Protokollide keel teeb raamistikke kiiremaks ja usaldusväärsemaks ning lihtsustab protokollide arendamist ja haldamist. Kirjeldame mõlemad keeled nii formaalselt kui mitteformaalselt. Näitame, kuidas mitmed rakendused ja prototüübid saavad neist keeltest kasu.Secure multi-party computation is a technology that allows several independent parties to cooperatively process their private data without revealing any secrets. If private inputs are given in encrypted form then the results will also be encrypted, and at no stage during processing are values ever decrypted. As a theoretical concept, the technology has been around since the 1980s, but the first practical implementations arose a bit more than a decade ago. Since then, secure multi-party computation has been used in practical applications, and has been established as an important method of data protection. Developing applications that use secure multi-party computation is challenging. The tools that help with development are still very young and the frameworks are often too slow for practical applications. Currently only experts in cryptography are able to develop secure multi-party applications. In this thesis we look how to improve secure multy-party computation frame- works and make the applications easier to develop. We claim that domain-specific programming languages enable to build secure multi-party applications and frame- works that are at the same time usable, efficient, maintainable, trustworthy, and practically scalable. The contribution of this thesis is the introduction of two new programming languages for secure multi-party computation. The SecreC 2 language makes secure multi-party computation application development easier, ensuring that the applications are secure and enabling them to be efficient. The second language is for developing low-level secure computation protocols. This language was created for improving secure multi-party computation frameworks. It makes the frameworks faster and more trustworthy, and protocols easier to develop and maintain. We give give both a formal and an informal overview of the two languages and see how they benefit multi-party applications and prototypes

A Secure Multi-Party Computation Protocol Suite Inspired by Shamir’s Secret Sharing Scheme

Tänapäeva maailm on täis saladusi. Mõnikord me sooviks teada midagi nende kohta ilma oma saladusi avalikustamata. Näiteks võib kedagi huvitada, kas ta on rikkam kui mõni tema sõber. Valitsusi võib huvitada, et nende satelliidid ei satuks kokkupõrkesse, kuid samas ei tahaks nad välismaailmale oma strateegiliste satelliitide liikumise kohta liiga palju infot jagada. Turvaline ühisarvutus võimaldab osapooltel ühiselt arvutada mingi funktsiooni väärtusi ilma sisendeid avalikustamata. Sharemind on üks praktiline raamistik turvaliste ühisarvutuste tegemiseks. Selles töös loomegi protokollide komplekti Sharemindi jaoks. Meie protokollid on inspireeritud Shamiri ühissalastusest, mis võimaldab saladusi osadeks jagada. Me anname algoritmid liitmise, korrutamise ja võrdlustehete jaoks koos vastavate turvatõestustega. Lisaks võrdleme realiseeritud protokollide jõudlust juba varasemalt olemasoleva protokollistikuga. Esialgsed keerukus- ja jõudlustulemused on lubavad, kuid on arenguruumi.The world today is full of secrets. Sometimes, we would like to know something about them without revealing the secrets themselves. For example, whether I have more money than my friend or whether two satellites would collide without publishing their moving trajectories. Secure multi-party computation allows us to jointly compute some functions while keeping the privacy of our inputs. Sharemind is a practical framework for performing secure multi-party computations. In this work, we added a protocol suite to Sharemind. This protocol suite was inspired by Shamir's secret sharing scheme, which describes a way to divide a secret into pieces. We describe algorithms for addition, multiplication, equality-testing and less-than comparison. We also give correctness and security proofs for the protocols. The resulting implementations were compared to an existing protocol suite inspired by additive secret sharing. The initial complexities and benchmarking results are promising, but there is room for improvement

Turvalisel ühisarvutusel põhinev privaatsust säilitav statistiline analüüs

Väitekirja elektrooniline versioon ei sisalda publikatsioone.Kaasaegses ühiskonnas luuakse inimese kohta digitaalne kirje kohe pärast tema sündi. Sellest hetkest alates jälgitakse tema käitumist ning kogutakse andmeid erinevate eluvaldkondade kohta. Kui kasutate poes kliendikaarti, käite arsti juures, täidate maksudeklaratsiooni või liigute lihtsalt ringi mobiiltelefoni taskus kandes, koguvad ning salvestavad firmad ja riigiasutused teie tundlikke andmeid. Vahel anname selliseks jälitustegevuseks vabatahtlikult loa, et saada mingit kasu. Näiteks võime saada soodustust, kui kasutame kliendikaarti. Teinekord on meil vaja teha keeruline otsus, kas loobuda võimalusest teha mobiiltelefonikõnesid või lubada enda jälgimine mobiilimastide kaudu edastatava info abil. Riigiasutused haldavad infot meie tervise, hariduse ja sissetulekute kohta, et meid paremini ravida, harida ja meilt makse koguda. Me loodame, et meie andmeid kasutatakse mõistlikult, aga samas eeldame, et meie privaatsus on tagatud. Käesolev töö uurib, kuidas teostada statistilist analüüsi nii, et tagada üksikisiku privaatsus. Selle eesmärgi saavutamiseks kasutame turvalist ühisarvutust. See krüptograafiline meetod lubab analüüsida andmeid nii, et üksikuid väärtuseid ei ole kunagi võimalik näha. Hoolimata sellest, et turvalise ühisarvutuse kasutamine on aeganõudev protsess, näitame, et see on piisavalt kiire ja seda on võimalik kasutada isegi väga suurte andmemahtude puhul. Me oleme teinud võimalikuks populaarseimate statistilise analüüsi meetodite kasutamise turvalise ühisarvutuse kontekstis. Me tutvustame privaatsust säilitavat statistilise analüüsi tööriista Rmind, mis sisaldab kõiki töö käigus loodud funktsioone. Rmind sarnaneb tööriistadele, millega statistikud on harjunud. See lubab neil viia läbi uuringuid ilma, et nad peaksid üksikasjalikult tundma allolevaid krüptograafilisi protokolle. Kasutame dissertatsioonis kirjeldatud meetodeid, et valmistada ette statistiline uuring, mis ühendab kaht Eesti riiklikku andmekogu. Uuringu eesmärk on teada saada, kas Eesti tudengid, kes töötavad ülikooliõpingute ajal, lõpetavad nominaalajaga väiksema tõenäosusega kui nende õpingutele keskenduvad kaaslased.In a modern society, from the moment a person is born, a digital record is created. From there on, the person’s behaviour is constantly tracked and data are collected about the different aspects of his or her life. Whether one is swiping a customer loyalty card in a store, going to the doctor, doing taxes or simply moving around with a mobile phone in one’s pocket, sensitive data are being gathered and stored by governments and companies. Sometimes, we give our permission for this kind of surveillance for some benefit. For instance, we could get a discount using a customer loyalty card. Other times we have a difficult choice – either we cannot make phone calls or our movements are tracked based on cellular data. The government tracks information about our health, education and income to cure us, educate us and collect taxes. We hope that the data are used in a meaningful way, however, we also have an expectation of privacy. This work focuses on how to perform statistical analyses in a way that preserves the privacy of the individual. To achieve this goal, we use secure multi-­‐party computation. This cryptographic technique allows data to be analysed without seeing the individual values. Even though using secure multi-­‐party computation is a time-­‐consuming process, we show that it is feasible even for large-­‐scale databases. We have developed ways for using the most popular statistical analysis methods with secure multi-­‐party computation. We introduce a privacy-­‐preserving statistical analysis tool called Rmind that contains all of our resulting implementations. Rmind is similar to tools that statistical analysts are used to. This allows them to carry out studies on the data without having to know the details of the underlying cryptographic protocols. The methods described in the thesis are used in practice to prepare for running a statistical study on large-­‐scale real-­‐life data to find out whether Estonian students who are working during university studies are less likely to graduate in nominal time

SkyEye: A Traceable Scheme for Blockchain

Many studies focus on the blockchain privacy protection. Unfortunately, the privacy protection brings some issues (e.g., money-laundering problem). Tracing users\u27 identities is a critical step in addressing these issues. When each user\u27s identity in the blockchain data is determined, the regulator can do some regulatory operations (such as Big Data analysis) to decide who should be punished or who should own the lost data. In this paper, we propose SkyEye, a traceable scheme for blockchain, that can be applied to a class of blockchain application. SkyEye enables the regulator to trace users\u27 identities. Moreover, we demonstrate the security of SkyEye under specific cryptographic assumptions. Finally, we implement two prototypes of SkyEye, and evaluate the running time and related data storage requirements by performing the aforementioned prototypes