Frigate: A Validated, Extensible, and Efficient Compiler and Interpreter for Secure Computation

Mood, Benjamin; Gupta, Debayan; Carter, Henry; Butler, Kevin; Traynor, Patrick

doi:10.1109/eurosp.2016.20

Cited by 67 publications

(57 citation statements)

References 37 publications

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“…When presenting Fairplay [39], the authors realized the need for compilation for MPC, and thus not only provided a protocol implementation but also a circuit compiler for a domain specific language (DSL). Subsequently, multiple Boolean circuit compilers have been presented that improved the compilation result by using logic minimization techniques (e.g., TinyGarble [17,48]), improved the compiler's scalability (e.g., KSS [35], Obliv-C [52], and Frigate [41]), targeted standard programming languages (e.g., CBMC-GC [26], and PCF [34]), or provided a formally verified tool-chain (e.g., Circ-Gen [1]). Examples for arithmetic circuit compilers that target linear secret sharing-based MPC protocols are the Sharemind compiler [7] for a DSL and the PICCO compiler [54] for C.…”

Section: Compilation For Mpcmentioning

confidence: 99%

“…A faster compile time can be achieved, under a (often significant) circuit-size trade-off, when not optimizing across function or loop boundaries. For example, the circuit compiler Frigate [41] follows this approach. To achieve the best of both worlds, we propose a time-constrained multi-pass optimization routine, which can be interrupted at any point in time.…”

Section: Source Code Optimization and Loopmentioning

confidence: 99%

See 1 more Smart Citation

HyCC

Büscher

Demmler

Katzenbeisser

et al. 2018

Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security

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While secure multi-party computation (MPC) is a vibrant research topic and a multitude of practical MPC applications have been presented recently, their development is still a tedious task that requires expert knowledge. Previous works have made first steps in compiling high-level descriptions from various source descriptions into MPC protocols, but only looked at a limited set of protocols. In this work we present HyCC, a tool-chain for automated compilation of ANSI C programs into hybrid protocols that efficiently and securely combine multiple MPC protocols with optimizing compilation, scheduling, and partitioning. As a result, our compiled protocols are able to achieve performance numbers that are comparable to hand-built solutions. For the MiniONN neural network (Liu et al., CCS 2017), our compiler improves performance of the resulting protocol by more than a factor of 3. Thus, for the first time, highly efficient hybrid MPC becomes accessible for developers without cryptographic background.

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Section: Compilation For Mpcmentioning

confidence: 99%

Section: Source Code Optimization and Loopmentioning

confidence: 99%

HyCC

Büscher

Demmler

Katzenbeisser

et al. 2018

Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security

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“…ABY Framework [51]: Among the variety of Secure Multi-Party Computation (SMPC) frameworks, e.g., [52,51,53,54], we choose the ABY framework [51] for its efficiency and the ability to mix multiple SMPC protocols. ABY is a state-of-the-art framework for Secure Two-Party Computation (STPC).…”

Section: B Secure Multi-party Computationmentioning

confidence: 99%

PILOT: Practical Privacy-Preserving Indoor Localization Using OuTsourcing

Järvinen

Leppäkoski

Lohan

et al. 2019

2019 IEEE European Symposium on Security and Privacy (EuroS&P)

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In the last decade, we observed a constantly growing number of Location-Based Services (LBSs) used in indoor environments, such as for targeted advertising in shopping malls or finding nearby friends. Although privacy-preserving LBSs were addressed in the literature, there was a lack of attention to the problem of enhancing privacy of indoor localization, i.e., the process of obtaining the users' locations indoors and, thus, a prerequisite for any indoor LBS.In this work we present PILOT, the first practically efficient solution for Privacy-Preserving Indoor Localization (PPIL) that was obtained by a synergy of the research areas indoor localization and applied cryptography. We design, implement, and evaluate protocols for Wi-Fi fingerprint-based PPIL that rely on 4 different distance metrics. To save energy and network bandwidth for the mobile end devices in PPIL, we securely outsource the computations to two non-colluding semi-honest parties. Our solution mixes different secure two-party computation protocols and we design size-and depth-optimized circuits for PPIL. We construct efficient circuit building blocks that are of independent interest: Single Instruction Multiple Data (SIMD) capable oblivious access to an array with low circuit depth and selection of the k-Nearest Neighbors with small circuit size. Additionally, we reduce Received Signal Strength (RSS) values from 8 bits to 4 bits without any significant accuracy reduction. Our most efficient PPIL protocol is 553x faster than that of Li et al. (INFOCOM'14) and 500x faster than that of Ziegeldorf et al. (WiSec'14). Our implementation on commodity hardware has practical run-times of less than 1 second even for the most accurate distance metrics that we consider, and it can process more than half a million PPIL queries per day. 448 2019 IEEE European Symposium on Security and Privacy (EuroS&P)

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“…ObliVM [14] is another ORAM-based secure two-party computation compiler that transforms programs written in high level abstractions to optimized garbled circuit implementations. Finally, a recent compiler Frigate [17] was designed to guarantee correctness of programs compiled into circuits for secure two-party computation.…”

Section: Related Workmentioning

confidence: 99%

Implementing Support for Pointers to Private Data in a General-Purpose Secure Multi-Party Compiler

Zhang

Blanton

Almashaqbeh

2017

ACM Trans. Priv. Secur.

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Recent compilers allow a general-purpose program (written in a conventional programming language) that handles private data to be translated into secure distributed implementation of the corresponding functionality. The resulting program is then guaranteed to provably protect private data using secure multi-party computation techniques. The goals of such compilers are generality, usability, and efficiency, but the complete set of features of a modern programming language has not been supported to date by the existing compilers. In particular, recent compilers PICCO and the two-party ANSI C compiler strive to translate any C program into its secure multi-party implementation, but currently lack support for pointers and dynamic memory allocation, which are important components of many C programs. In this work, we mitigate the limitation and add support for pointers to private data and consequently dynamic memory allocation to the PICCO compiler, enabling it to handle a more diverse set of programs over private data. Because doing so opens up a new design space, we investigate the use of pointers to private data (with known as well as private locations stored in them) in programs and report our findings. Besides dynamic memory allocation, we examine other important topics associated with common pointer use such as reference by pointer/address, casting, and building various data structures in the context of secure multi-party computation. This results in enabling the compiler to automatically translate a user program that uses pointers to private data into its distributed implementation that provably protects private data throughout the computation. We empirically evaluate the constructions and report on performance of representative programs.

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Frigate: A Validated, Extensible, and Efficient Compiler and Interpreter for Secure Computation

Cited by 67 publications

References 37 publications

HyCC

HyCC

PILOT: Practical Privacy-Preserving Indoor Localization Using OuTsourcing

Implementing Support for Pointers to Private Data in a General-Purpose Secure Multi-Party Compiler

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