Domain-Polymorphic Programming of Privacy-Preserving Applications

Secure Multiparty Computation (SMC) is seen as one of the main enablers for secure outsourcing of computation. Currently, there are many different SMC techniques (garbled circuits, secret sharing, homomorphic encryption, etc.) and none of them is clearly superior to others in terms of efficiency, security guarantees, ease of implementation, etc. For maximum efficiency, and for obeying the trust policies, a privacy-preserving application may wish to use several different SMC techniques for different operations it performs. A straightforward implementation of this application may result in a program that (i) contains a lot of duplicated code, differing only in the used SMC technique; (ii) is difficult to maintain, if policies or SMC implementations change; and (iii) is difficult to reuse in similar applications using different SMC techniques. In this paper, we propose a programming language with associated compilation techniques for simple orchestration of multiple SMC techniques and multiple protection domains. It is a simple imperative language with function calls where the types of data items are annotated with protection domains and where the function declarations may be domain-polymorphic. This allows most of the program code working with private data to be written in a SMC-technique-agnostic manner. It also allows rapid deployment of new SMC techniques and implementations in existing applications. We have implemented the compiler for the language, integrated it with an existing SMC framework, and are currently using it for new privacy-preserving applications

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

An Improved Type System for a Privacy-aware Programming Language and its Practical Applications

Privaatseid andmeid on tarvis analüüsida või töödelda mitmes valdkonnas, näiteks tehes poliitilisi otsusi kasutades riiklikke andmekogusid või pakkudes pilvepõhiseid teenuseid. Sharemind on raamistik turvalisust säilitavate rakenduste arendamiseks, mis võimaldab andmeid analüüsida ilma üksikuid väärtuseid avaldamata. Sharemind kasutab selleks turvalise ühisarvutuse tehnoloogiat. Sharemindi raamistikku kasutavad programmid on kirjutatud programmeerimiskeeles nimega SecreC. Sharemind ja SecreC toetavad erinevaid turvalise ühisarvutuse meetodeid, mida nimetame turvaaladeks. Erinevatel turvaaladel on erinevad turvagarantiid ja efektiivsus ning turvaala valik sõltub konkreetse rakenduse vajadustest, mistõttu peaks SecreC toetama erinevate turvaalade kasutamist vastavalt rakenduse nõuetele. Töö eesmärk on võimaldada SecreC keelele turvaalade lisamist lubades programmeerijal kirjeldada turvaala andmetüübid, aritmeetilised tehted ja tüübiteisendused SecreC keeles. Töö autor lõi keele täiendustele formaalselt kirjeldatud tüübisüsteemi, teostas muudatused SecreC kompilaatoris, kirjeldas muudatuste praktilisi rakendusi, tekkivaid uusi probleeme ja nende võimalikke lahendusi.Confidential data needs to be processed in many areas, for example when making policy decisions using goverment databases or when providing cloud-based services. Sharemind is a framework for developing privacy-preserving applications which allows data to be analysed without revealing individual values. Sharemind uses a technology called secure multi-party computation. Programs using the Sharemind framework are written in a programming language called SecreC. Sharemind and SecreC are designed to support multiple secure multi-party computation methods which we call protection domain kinds. Different protection domain kinds have different security guarantees and performance characteristics and the decision about which one to use depends on the problem at hand which means SecreC should support different protection domain kinds that solve the needs of different applications. The goal of this thesis is to make it easier to add protection domain kinds to the SecreC language by allowing the programmer to define the protection domain kind data types, arithmetic operations and type conversions in the SecreC language without changing the compiler. The author developed a formal type system for the proposed language extensions, implemented them in the SecreC language compiler, described practical applications, open problems and proposed solutions

Language-Based Differential Privacy with Accuracy Estimations and Sensitivity Analyses

This thesis focuses on the development of programming frameworks to enforce, by construction, desirable properties of software systems. Particularly, we are interested in enforcing differential privacy -- a mathematical notion of data privacy -- while statically reasoning about the accuracy of computations, along with deriving the sensitivity of arbitrary functions to further strengthen the expressiveness of these systems. To this end, we first introduce DPella, a programming framework for differentially-private queries that allows reasoning about the privacy and accuracy of data analyses. DPella provides a novel component that statically tracks the accuracy of different queries. This component leverages taint analysis to infer statistical independence of the different noises that were added to ensure the privacy of the overall computation. As a result, DPella allows analysts to implement privacy-preserving queries and adjust the privacy parameters to meet accuracy targets or vice-versa.In the context of differentially-private systems, the sensitivity of a function determines the amount of noise needed to achieve a desired level of privacy. However, establishing the sensitivity of arbitrary functions is non-trivial. Consequently, systems such as DPella provided a limited set of functions -- whose sensitivity is known -- to apply over sensitive data, thus hindering the expressiveness of the language. To overcome this limitation, we propose a new approach to derive proofs of sensitivity in programming languages with support for polymorphism. Our approach enriches base types with information about the metric relation between values and applies parametricity to derive proof of a function\u27s sensitivity. These ideas are formalized in a sound calculus and implemented as a Haskell library called Spar, enabling programmers to prove the sensitivity of their functions through type-checking alone.Overall, this thesis contributes to the development of expressive programming frameworks for data analysis with privacy and accuracy guarantees. The proposed approaches are feasible and effective, as demonstrated through the implementation of DPella and Spar

Developing a Deterministic Polymorphic Circuit Generator Using Random Boolean Logic Expansion

Securing applications on untrusted platforms can involve protection against legitimate endusers who act in the role of malicious reverse engineers and hackers. Such adversaries have access to the full execution environment of programs, whether the program comes in the form of software or hardware. In this thesis, we consider the nature of obfuscating algorithms that perform iterative, stepwise transformation of programs into more complex forms that are intended to increase the complexity (time, resources) for malicious reverse engineers. We consider simple Boolean logic programs as the domain of interest and examine a specific transformation technique known as Iterative Selection and Replacement (ISR), which represents a practical, syntactic approach for obfuscation. Specifically, we focus on improving the security of ISR by maximizing the flexibility and potential security of the replacement step of the algorithm which can be formulated in the following question: Given a selection of Boolean logic gates (i.e., a subcircuit), how can we produce a semantically equivalent (polymorphic) version of the subcircuit such that the distribution of potential replacements represents a random, uniform distribution from the set of all possible replacements? This practical question is related to the theoretic study of indistinguishability obfuscation, where a transformer for a class of circuits guarantees that given any two semantically equivalent circuits from the class, the distribution of variants from their obfuscation are computationally indistinguishable. Ideally, polymorphic circuits that follow a random, uniform distribution provide stronger protection against malicious analyzers that target identification of distinct patterns as a basis for deobfuscation and simplification. We introduce a novel approach for polymorphic circuit replacement called Random Boolean Logic Expansion (RBLE), which applies Boolean logic laws (of reduction) in reverse. We compare this approach against another proposed method of polymorphic replacement that relies on static circuit libraries. As a contribution, we show the strengths and weaknesses of each approach, examine initial results from empirical studies to estimate the uniformity of polymorphic distributions, and provide the argument for how such algorithms can be readily applied in software contexts. RBLE provides a unique method to generate polymorphic variants of arbitrary input, output, and gate size. We report initial findings for studying variants produced by this method and, from empirical evaluation, show that RBLE has promise for generating distributions of unique, uniform circuits when size is unconstrained, but for targeted size distributions, the approach requires adjustment for reaching potential circuit variant

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