Applying Secure Multi-party Computation in Practice

In this work, we present solutions for technical difficulties in deploying secure multi-party computation in real-world applications. We will first give a brief overview of the current state of the art, bring out several shortcomings and address them. The main contribution of this work is an end-to-end process description of deploying secure multi-party computation for the first large-scale registry-based statistical study on linked databases. Involving large stakeholders like government institutions introduces also some non-technical requirements like signing contracts and negotiating with the Data Protection Agency

Secure multi-party data analysis: end user validation and practical experiments

Research papers on new secure multi-party computation protocols rarely confirm the need for the developed protocol with its end users. One challenge in the way of such validation is that it is hard to explain the benefits of secure multi-party computation to non-experts. We present a method that we used to explain the application models of secure multi-party computation to a diverse group of end users in several professional areas. In these interviews, we learned that the potential users were curious about the possibility of using secure multi-party computation to share and statistically analyse private data. However, they also had concerns on how the new technology will change the data analysis processes. Inspired by this, we implemented a secure multi-party computation prototype that calculates statistical functions in the same way as popular data analysis packages like R, SAS, SPSS and Stata. Finally, we validated the practical feasibility of this application by conducting an experimental study that combined tax records with education records

Secure multi-party computation for analytics deployed as a lightweight web application

We describe the definition, design, implementation, and deployment of a secure multi-party computation protocol and web application. The protocol and application allow groups of cooperating parties with minimal expertise and no specialized resources to compute basic statistical analytics on their collective data sets without revealing the contributions of individual participants. The application was developed specifically to support a Boston Women’s Workforce Council (BWWC) study of wage disparities within employer organizations in the Greater Boston Area. The application has been deployed successfully to support two data collection sessions (in 2015 and in 2016) to obtain data pertaining to compensation levels across genders and demographics. Our experience provides insights into the particular security and usability requirements (and tradeoffs) a successful “MPC-as-a-service” platform design and implementation must negotiate.We would like to acknowledge all the members of the Boston Women’s Workforce Council, and to thank in particular MaryRose Mazzola, Christina M. Knowles, and Katie A. Johnston, who led the efforts to organize participants and deploy the protocol as part of the 100% Talent: The Boston Women’s Compact [31], [32] data collections. We also thank the Boston University Initiative on Cities (IOC), and in particular Executive Director Katherine Lusk, who brought this potential application of secure multi-party computation to our attention. The BWWC, the IOC, and several sponsors contributed funding to complete this work. Support was also provided in part by Smart-city Cloud-based Open Platform and Ecosystem (SCOPE), an NSF Division of Industrial Innovation and Partnerships PFI:BIC project under award #1430145, and by Modular Approach to Cloud Security (MACS), an NSF CISE CNS SaTC Frontier project under award #1414119

Applications of Secure Multiparty Computation

We generate and gather a lot of data about ourselves and others, some of it highly confidential. The collection, storage and use of this data is strictly regulated by laws, but restricting the use of data often limits the benefits which could be obtained from its analysis. Secure multi-party computation (SMC), a cryptographic technology, makes it possible to execute specific programs on confidential data while ensuring that no other sensitive information from the data is leaked. SMC has been the subject of academic study for more than 30 years, but first attempts to use it for actual computations in the early 2000s – although theoretically efficient – were initially not practicable. However, improvements in the situation have made possible the secure solving of even relatively large computational tasks. This book describes how many different computational tasks can be solved securely, yet efficiently. It describes how protocols can be combined to larger applications, and how the security-efficiency trade-offs of different components of an SMC application should be chosen. Many of the results described in this book were achieved as part of the project Usable and Efficient Secure Multi-party Computation (UaESMC), which was funded by the European Commission. The book will be of interest to all those whose work involves the secure analysis of confidential data

Secure Multi-Party Computation In Practice

Secure multi-party computation (MPC) is a cryptographic primitive for computing on private data. MPC provides strong privacy guarantees, but practical adoption requires high-quality application design, software development, and resource management. This dissertation aims to identify and reduce barriers to practical deployment of MPC applications. First, the dissertation evaluates the design, capabilities, and usability of eleven state-of-the-art MPC software frameworks. These frameworks are essential for prototyping MPC applications, but their qualities vary widely; the survey provides insight into their current abilities and limitations. A comprehensive online repository augments the survey, including complete build environments, sample programs, and additional documentation for each framework. Second, the dissertation applies these lessons in two practical applications of MPC. The first addresses algorithms for assessing stability in financial networks, traditionally designed in a full-information model with a central regulator or data aggregator. This case study describes principles to transform two such algorithms into data-oblivious versions and benchmark their execution under MPC using three frameworks. The second aims to enable unlinkability of payments made with blockchain-based cryptocurrencies. This study uses MPC in conjunction with other privacy techniques to achieve unlinkability in payment channels. Together, these studies illuminate the limitations of existing software, develop guidelines for transforming non-private algorithms into versions suitable for execution under MPC, and illustrate the current practical feasibility of MPC as a solution to a wide variety of applications

Students and Taxes: a Privacy-Preserving Social Study Using Secure Computation

We describe the use of secure multi-party computation for performing a large-scale privacy-preserving statistical study on real government data. In 2015, statisticians from the Estonian Center of Applied Research (CentAR) conducted a big data study to look for correlations between working during university studies and failing to graduate in time. The study was conducted by linking the database of individual tax payments from the Estonian Tax and Customs Board and the database of higher education events from the Ministry of Education and Research. Data collection, preparation and analysis were conducted using the Sharemind secure multi-party computation system that provided end-to-end cryptographic protection to the analysis. Using ten million tax records and half a million education records in the analysis, this is the largest cryptographically private statistical study ever conducted on real data

Rmind: a tool for cryptographically secure statistical analysis

Secure multi-party computation platforms are becoming more and more practical. This has paved the way for privacy-preserving statistical analysis using secure multi-party computation. Simple statistical analysis functions have been emerging here and there in literature, but no comprehensive system has been compiled. We describe and implement the most used statistical analysis functions in the privacy-preserving setting including simple statistics, t-test, $\chi^{2}$ test, Wilcoxon tests and linear regression. We give descriptions of the privacy-preserving algorithms and benchmark results that show the feasibility of our solution

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