DEMO: integrating MPC in big data workflows

Secure multi-party computation (MPC) allows multiple parties to perform a joint computation without disclosing their private inputs. Many real-world joint computation use cases, however, involve data analyses on very large data sets, and are implemented by software engineers who lack MPC knowledge. Moreover, the collaborating parties -- e.g., several companies -- often deploy different data analytics stacks internally. These restrictions hamper the real-world usability of MPC. To address these challenges, we combine existing MPC frameworks with data-parallel analytics frameworks by extending the Musketeer big data workflow manager [4]. Musketeer automatically generates code for both the sensitive parts of a workflow, which are executed in MPC, and the remainder of the computation, which runs on scalable, widely-deployed analytics systems. In a prototype use case, we compute the Herfindahl-Hirschman Index (HHI), an index of market concentration used in antitrust regulation, on an aggregate 156GB of taxi trip data over five transportation companies. Our implementation computes the HHI in about 20 minutes using a combination of Hadoop and VIFF [1], while even "mixed mode" MPC with VIFF alone would have taken many hours. Finally, we discuss future research questions that we seek to address using our approach

ARM2GC: Succinct Garbled Processor for Secure Computation

We present ARM2GC, a novel secure computation framework based on Yao's Garbled Circuit (GC) protocol and the ARM processor. It allows users to develop privacy-preserving applications using standard high-level programming languages (e.g., C) and compile them using off-the-shelf ARM compilers (e.g., gcc-arm). The main enabler of this framework is the introduction of SkipGate, an algorithm that dynamically omits the communication and encryption cost of the gates whose outputs are independent of the private data. SkipGate greatly enhances the performance of ARM2GC by omitting costs of the gates associated with the instructions of the compiled binary, which is known by both parties involved in the computation. Our evaluation on benchmark functions demonstrates that ARM2GC not only outperforms the current GC frameworks that support high-level languages, it also achieves efficiency comparable to the best prior solutions based on hardware description languages. Moreover, in contrast to previous high-level frameworks with domain-specific languages and customized compilers, ARM2GC relies on standard ARM compiler which is rigorously verified and supports programs written in the standard syntax.Comment: 13 page

Scather: programming with multi-party computation and MapReduce

We present a prototype of a distributed computational infrastructure, an associated high level programming language, and an underlying formal framework that allow multiple parties to leverage their own cloud-based computational resources (capable of supporting MapReduce [27] operations) in concert with multi-party computation (MPC) to execute statistical analysis algorithms that have privacy-preserving properties. Our architecture allows a data analyst unfamiliar with MPC to: (1) author an analysis algorithm that is agnostic with regard to data privacy policies, (2) to use an automated process to derive algorithm implementation variants that have different privacy and performance properties, and (3) to compile those implementation variants so that they can be deployed on an infrastructures that allows computations to take place locally within each participant’s MapReduce cluster as well as across all the participants’ clusters using an MPC protocol. We describe implementation details of the architecture, discuss and demonstrate how the formal framework enables the exploration of tradeoffs between the efficiency and privacy properties of an analysis algorithm, and present two example applications that illustrate how such an infrastructure can be utilized in practice.This work was supported in part by NSF Grants: #1430145, #1414119, #1347522, and #1012798

A formal treatment of the role of verified compilers in secure computation

First online 19 November 2021Secure multiparty computation (SMC) allows for complex computations over encrypted data. Privacy concerns for cloud applications makes this a highly desired technology and recent performance improvements show that it is practical. To make SMC accessible to non-experts and empower its use in varied applications, many domain-specific compilers are being proposed.We review the role of these compilers and provide a formal treatment of the core steps that they perform to bridge the abstraction gap between high-level ideal specifications and efficient SMC protocols. Our abstract framework bridges this secure compilation problem across two dimensions: 1) language-based source- to target-level semantic and efficiency gaps, and 2) cryptographic ideal- to real-world security gaps. We link the former to the setting of certified compilation, paving the way to leverage long-run efforts such as CompCert in future SMC compilers. Security is framed in the standard cryptographic sense. Our results are supported by a machine-checked formalisation carried out in EasyCrypt. (c) 2021 Elsevier Inc. All rights reserved.- Acknowledgements Jose Bacelar Almeida was partially funded by the PassCert project, a CMU Portugal Exploratory Project funded by Fundacao para a Ciencia e Tecnologia (FCT) , with reference CMU/TIC/0006/2019

FUSE – Flexible File Format and Intermediate Representation for Secure Multi-Party Computation

Secure Multi-Party Computation (MPC) is continuously becoming more and more practical. Many optimizations have been introduced, making MPC protocols more suitable for solving real-world problems. However, the MPC protocols and optimizations are usually implemented as a standalone proof of concept or in an MPC framework and are tightly coupled with special-purpose circuit formats, such as Bristol Format. This makes it very hard and time-consuming to re-use algorithmic advances and implemented applications in a different context. Developing generic algorithmic optimizations is exceptionally hard because the available MPC tools and formats are not generic and do not provide the necessary infrastructure. In this paper, we present FUSE: A Framework for Unifying and Optimizing Secure Multi-Party Computation Implementations with Efficient Circuit Storage. FUSE provides a flexible intermediate representation (FUSE IR) that can be used across different platforms and in different programming languages, including C/C++, Java, Rust, and Python. We aim at making MPC tools more interoperable, removing the tight coupling between high-level compilers for MPC and specific MPC protocol engines, thus driving knowledge transfer. Our framework is inspired by the widely known LLVM compiler framework. FUSE is portable, extensible, and it provides implementation-agnostic optimizations. As frontends, we implement HyCC (CCS\u2718), the Bristol circuit format, and MOTION (TOPS\u2722), meaning that these can be automatically converted to FUSE IR. We implement several generic optimization passes, such as automatic subgraph replacement and vectorization, to showcase the utility and efficiency of our framework. Finally, we implement as backends MOTION and MP-SPDZ (CCS\u2720), so that FUSE IR can be run by these frameworks in an MPC protocol, as well as other useful backends for JSON output and the DOT language for graph visualization. With FUSE, it is possible to use any implemented frontend with any implemented backend and vice-versa. FUSE IR is not only efficient to work on and much more generic than any other format so far -- supporting, e.g., function calls, hybrid MPC protocols as well as user-defined building blocks, and annotations -- while maintaining backwards-compatibility, but also compact, with smaller storage size than even minimalistic formats such as Bristol already for a few hundred operations

GraphSC: Parallel secure computation made easy

Abstract-We propose introducing modern parallel programming paradigms to secure computation, enabling their secure execution on large datasets. To address this challenge, we present GraphSC, a framework that (i) provides a programming paradigm that allows non-cryptography experts to write secure code; (ii) brings parallelism to such secure implementations; and (iii) meets the needs for obliviousness, thereby not leaking any private information. Using GraphSC, developers can efficiently implement an oblivious version of graph-based algorithms (including sophisticated data mining and machine learning algorithms) that execute in parallel with minimal communication overhead. Importantly, our secure version of graph-based algorithms incurs a small logarithmic overhead in comparison with the non-secure parallel version. We build GraphSC and demonstrate, using several algorithms as examples, that secure computation can be brought into the realm of practicality for big data analysis. Our secure matrix factorization implementation can process 1 million ratings in 13 hours, which is a multiple order-of-magnitude improvement over the only other existing attempt, which requires 3 hours to process 16K ratings

Compilation Techniques for Actively Secure Mixed Mode Two Party Computation

Secure multiparty computation allows two or more parties to compute a function without leaking unnecessary information about their inputs to other parties. In traditional secure multiparty computation protocols, the function is represented as a circuit and each gate of the circuit is securely computed. The mixed mode model is a generalization where only some gates are computed securely, and other gates are computed in a local, unsecured manner. There are computations where mixed mode protocols are known to be just as secure and much more efficient, and so it is natural to ask if it is possible to automatically construct optimized mixed mode secure protocols for a given function. Previous results describe powerful compilation techniques to transform circuits into efficient mixed mode protocols, but the results are only secure against very restricted (passive) adversaries. These passively secure protocols can be secured against active adversaries using extensions of classic secure multiparty computation compilation techniques. However, this comes with a significant loss of concrete efficiency, which negates the mixed mode efficiency advantages. In this thesis, we describe novel techniques that can efficiently compile mixed mode two party protocols from passive to active security. The techniques exploit structural properties of the underlying circuits to reduce the overhead of compilation without compromising the security. The gain in efficiency varies based on the circuit that is being compiled, and although for some circuits the techniques will yield no gains, for others the resulting secure protocols have exponentially lower computation and communication cost

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