• Corpus ID: 3629461

EzPC: Programmable, Efficient, and Scalable Secure Two-Party Computation

  title={EzPC: Programmable, Efficient, and Scalable Secure Two-Party Computation},
  author={Nishanth Chandran and Divya Gupta and Aseem Rastogi and Rahul Sharma and Shardul Tripathi},
  journal={IACR Cryptol. ePrint Arch.},
We present EzPC: a secure two-party computation (2PC) framework that generates efficient 2PC protocols from high-level, easyto-write programs. [] Key Method Our compiler is the first to generate protocols that combine both arithmetic sharing and garbled circuits for better performance. We empirically demonstrate that the protocols generated by our framework match or outperform (up to 19x) recent works that provide hand-crafted protocols for various functionalities such as secure prediction and matrix…

LLVM-Based Circuit Compilation for Practical Secure Computation

This paper develops an LLVM optimizer suite consisting of multiple transform passes that operate on the LLVM intermediate representation (IR) and gradually lower functions to circuit level and empirically measures the quality of the compilation results and compares them to the state-of-the-art specialized MPC compiler HyCC.

SoK: General Purpose Compilers for Secure Multi-Party Computation

This work surveys general-purpose compilers for secure multi-party computation and evaluates eleven systems on a range of criteria, including language expressibility, capabilities of the cryptographic back-end, and accessibility to developers.

HyCC: Compilation of Hybrid Protocols for Practical Secure Computation

HyCC is presented, 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 that becomes accessible for developers without cryptographic background.

Zaphod: Efficiently Combining LSSS and Garbled Circuits in SCALE

Modifications to the MPC system SCALE-MAMBA are presented to enable the evaluation of garbled circuit (GC) based MPC functionalities and Linear Secret Sharing (LSSS) based MPs along side each other and to give a more efficient method for producing daBits than that presented in the work of Rotaru and Wood.

CostCO: An automatic cost modeling framework for secure multi-party computation

This paper proposes CostCO, the first automatic MPC cost modeling framework, which develops a novel API to interface with a variety of MPC protocols, and leverages domain-specific properties of MPc in order to enable efficient and automatic cost-model generation for a wide range ofMPC protocols.

CrypTen: Secure Multi-Party Computation Meets Machine Learning

Secure multi-party computation (MPC) allows parties to perform computations on data while keeping that data private. This capability has great potential for machine-learning applications: it

MOBIUS: Model-Oblivious Binarized Neural Networks

A privacy-preserving framework in which a computational resource provider receives encrypted data from a client and returns prediction results without decrypting the data, i.e., oblivious neural

Wys*: A DSL for Verified Secure Multi-party Computations

The first DSL to enable formal verification of MPC programs is presented, a new domain-specific language (DSL) for writing mixed-mode MPCs, and the necessary metatheory is mechanized to prove that the properties verified for the source programs carry over to the distributed, multi-party semantics.

Falcon: Honest-Majority Maliciously Secure Framework for Private Deep Learning

The experiments in the WAN setting show that over large networks and datasets, compute operations dominate the overall latency of MPC, as opposed to the communication.

GALA: Greedy ComputAtion for Linear Algebra in Privacy-Preserved Neural Networks

GALA efficiently reduces the cost for the HE-based linear computation, which is a critical building block in almost all of the recent frameworks for privacy-preserved neural networks, including GAZELLE, DELPHI, and CrypTFlow2, and can be a plug-and-play module integrated into these systems to further boost their efficiency.



TASTY: tool for automating secure two-party computations

TASTY is a new compiler that can generate protocols based on homomorphic encryption and efficient garbled circuits as well as combinations of both, which often yields the most efficient protocols available today.

Compiling Low Depth Circuits for Practical Secure Computation

With the rise of practical Secure Multi-party Computation protocols, compilers have been developed that create Boolean or Arithmetic circuits for MPC from functionality descriptions in a high-level language that have a round complexity that is dependent on the circuit’s depth.

Faster Secure Two-Party Computation Using Garbled Circuits

This work demonstrates several techniques for improving the running time and memory requirements of the garbled-circuit technique, resulting in an implementation of generic secure two-party computation that is significantly faster than any previously reported while also scaling to arbitrarily large circuits.

CompGC: Efficient Offline/Online Semi-honest Two-party Computation

This work introduces a new technique, component-based garbled circuits, for increasing the efficiency of secure two-party computation in the offline/online semi-honest setting, and finds that this technique results in roughly an order of magnitude performance improvement over standard garbled circuit-based secureTwo- party computation.

Secure two-party computations in ANSI C

A nonstandard use of the bit-precise model checker CBMC is used which enables us to translate C programs into equivalent Boolean circuits and modify the standard CBMC translation from programs into Boolean formulas whose variables correspond to the memory bits manipulated by the program.

L1 - An Intermediate Language for Mixed-Protocol Secure Computation

A new intermediate language (L1) is proposed for optimizing SC compilers which enables efficient implementation of special protocols potentially mixing several general SC protocols and it is shown that only a combined view on algorithm and cryptographic protocol can discover SCs with best run-time performance.

Automated Synthesis of Optimized Circuits for Secure Computation

This work presents how to use industrial-grade hardware synthesis tools to generate circuits that are not only optimized for size, but also for depth, required for secure computation protocols with non-constant round complexity, and shows how to easily obtain circuits for IEEE 754 compliant floating-point operations.

ObliVM: A Programming Framework for Secure Computation

This work develops various showcase applications such as data mining, streaming algorithms, graph algorithms, genomic data analysis, and data structures, and demonstrates the scalability of ObliVM to bigger data sizes.

Fairplay - Secure Two-Party Computation System

Fairplay is introduced, a full-fledged system that implements generic secure function evaluation (SFE) and provides a test-bed of ideas and enhancements concerning SFE, whether by replacing parts of it, or by integrating with it.

Information-Flow Control for Programming on Encrypted Data

This work presents an expressive core language for secure cloud computing, with primitive types, conditionals, standard functional features, mutable state, and a secrecy preserving form of general recursion, and proves that cloud implementations based on secret sharing, homomorphic encryption, or other alternatives satisfying the general definition meet precise security requirements.