Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications

We present Chameleon, a novel hybrid (mixed-protocol) framework for secure function evaluation (SFE) which enables two parties to jointly compute a function without disclosing their private inputs. Chameleon combines the best aspects of generic SFE protocols with the ones that are based upon additive secret sharing. In particular, the framework performs linear operations in the ring $\mathbb{Z}_{2^l}$ using additively secret shared values and nonlinear operations using Yao's Garbled Circuits or the Goldreich-Micali-Wigderson protocol. Chameleon departs from the common assumption of additive or linear secret sharing models where three or more parties need to communicate in the online phase: the framework allows two parties with private inputs to communicate in the online phase under the assumption of a third node generating correlated randomness in an offline phase. Almost all of the heavy cryptographic operations are precomputed in an offline phase which substantially reduces the communication overhead. Chameleon is both scalable and significantly more efficient than the ABY framework (NDSS'15) it is based on. Our framework supports signed fixed-point numbers. In particular, Chameleon's vector dot product of signed fixed-point numbers improves the efficiency of mining and classification of encrypted data for algorithms based upon heavy matrix multiplications. Our evaluation of Chameleon on a 5 layer convolutional deep neural network shows 133x and 4.2x faster executions than Microsoft CryptoNets (ICML'16) and MiniONN (CCS'17), respectively

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

Secure Multi-party Computation Protocols from a High-Level Programming Language

Turvalise ühisarvutuse abil on võimalik sooritada privaatsust säilitavaid arvutusi mitmelt osapoolelt kogutud andmetega. Tänapäeva digitaalses maailmas on andmete konfidentsiaalsuse tagamine üha raskemini teostatav. Turvalise ühisarvutuse meetodid nagu ühissalastus ja Yao sogastatud loogikaskeemid võimaldavad teostada privaatsust säilitavaid arvutusprotokolle, mis ei lekita konfidentsiaalseid sisendandmeid. Aditiivne ühissalastuse skeem on väga efektiivne algebraliste ringide tehete sooritamiseks fikseeritud bitilaiusega andmetüüpide peal. Samas on seda kasutades raske ehitada protokolle, mis nõuavad paindlikumaid bititaseme operatsioone. Yao sogastatud loogikaskeemide meetod töötab aga igasuguse bitilaiusega andmete peal ja võimaldab väärtustada mistahes Boole'i funktsioone. Neid kahte meetodit koos kasutades ehitame turvalise hübriidprotokolli, mis kujutab endast üldist meetodit privaatsust säilitavate arvutuste teostamiseks bitikaupa ühissalastatud andmete peal. Loogikaskeeme vajalikeks arvutusteks on lihtne saada kahe kaasaegse turvalise ühisarvutuse jaoks mõeldud kompilaatori abil, mis muundavad C programmi loogikaskeemiks --- PCF ja CBMC-GC. Meie hübriidprotokolli prototüüp privaatsust säilitaval arvutusplatvormil Sharemind saavutab praktilisi jõudlustulemusi, mis on võrreldavad teiste kaasaegsete lahendustega. Lisaks kahe osapoolega arvutustele pakub meie prototüüp võimekust teostada mitmekesiseid arvutusi üldises turvalise ühisarvutuse arvutusmudelis. Hübriidprotokoll ja loogikaskeemide kompilaatorid võimaldavad koos kasutades lihtsalt ja efektiivselt luua üldkasutatavaid turvalise ühisarvutuse protokolle mistahes Boole'i funktsioonide väärtustamiseks.Secure multi-party computation (SMC) enables privacy-preserving computations on data originating from a number of parties. In today's digital world, data privacy is increasingly more difficult to provide. With SMC methods like secret sharing and Yao's garbled circuits, it is possible to build privacy-preserving computational protocols that do not leak confidential inputs to other parties. The additive secret sharing scheme is very efficient for algebraic ring operations on fixed bit-length data types. However, it is difficult to build protocols that require robust bit-level manipulation. Yao's garbled circuits approach, in contrast, works on arbitrary bit-length data and allows the evaluation of any Boolean function. Combining the two methods, we build a secure hybrid protocol, which provides a general method for building arbitrary secure computations on bitwise secret-shared data. We are able to generate circuits for the protocol easily by using two state-of-the-art C to circuit compilers designed for SMC applications --- PCF and CBMC-GC. Our hybrid protocol prototype on the Sharemind privacy-preserving computational platform achieves practical performance comparable to other recent work. In addition to two-party computations, our prototype provides the ability to perform a set of diverse computations in a generic SMC computational model. The hybrid protocol together with the circuit compilers provides a simple and efficient toolchain to build general-purpose SMC protocols for evaluating any Boolean function

ARPA Whitepaper

We propose a secure computation solution for blockchain networks. The correctness of computation is verifiable even under malicious majority condition using information-theoretic Message Authentication Code (MAC), and the privacy is preserved using Secret-Sharing. With state-of-the-art multiparty computation protocol and a layer2 solution, our privacy-preserving computation guarantees data security on blockchain, cryptographically, while reducing the heavy-lifting computation job to a few nodes. This breakthrough has several implications on the future of decentralized networks. First, secure computation can be used to support Private Smart Contracts, where consensus is reached without exposing the information in the public contract. Second, it enables data to be shared and used in trustless network, without disclosing the raw data during data-at-use, where data ownership and data usage is safely separated. Last but not least, computation and verification processes are separated, which can be perceived as computational sharding, this effectively makes the transaction processing speed linear to the number of participating nodes. Our objective is to deploy our secure computation network as an layer2 solution to any blockchain system. Smart Contracts\cite{smartcontract} will be used as bridge to link the blockchain and computation networks. Additionally, they will be used as verifier to ensure that outsourced computation is completed correctly. In order to achieve this, we first develop a general MPC network with advanced features, such as: 1) Secure Computation, 2) Off-chain Computation, 3) Verifiable Computation, and 4)Support dApps' needs like privacy-preserving data exchange

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

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

Through the Looking-Glass: Benchmarking Secure Multi-Party Computation Comparisons for ReLU\u27s

Comparisons or Inequality Tests are an essential building block of Rectified Linear Unit functions (ReLU\u27s), ever more present in Machine Learning, specifically in Neural Networks. Motivated by the increasing interest in privacy-preserving Artificial Intelligence, we explore the current state of the art of privacy preserving comparisons over Multi-Party Computation (MPC). We then introduce constant round variations and combinations, which are compatible with customary fixed point arithmetic over MPC. Our main focus is implementation and benchmarking; hence, we showcase our contributions via an open source library, compatible with current MPC software tools. Furthermore, we include a comprehensive comparative analysis on various adversarial settings. Our results improve running times in practical scenarios. Finally, we offer conclusions about the viability of these protocols when adopted for privacy-preserving Machine Learning