EzPC: Programmable and Efficient Secure Two-Party Computation for Machine Learning

Chandran, Nishanth; Gupta, Divya; Rastogi, Aseem; Sharma, Rahul; Tripathi, Shardul

doi:10.1109/eurosp.2019.00043

Cited by 92 publications

(89 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…§2.2), where the programmer has to manually select the respective protocol per operation. Very recently, the authors of [14] proposed a solution for hybrid compilation of MPC protocols called EzPC. However, while their main motivation is similar to ours, our results differ in several key points.…”

Section: Compilation For Mpcmentioning

confidence: 99%

“…Previous MPC compilers either only targeted a single class of protocols, e.g., Yao's garbled circuits [26,39,48], the GMW protocol [9,17], or linear secret-sharing-based MPC [7], or the compilers required the developer to use specific annotations to mark which protocol is used for each statement, e.g., [18,24]. The only other compiler that addresses the compilation of a program using two MPC protocols (Yao's gabled circuits and arithmetic sharing) is EzPC [14]. However, EzPC only provides semi-automation for a domain specific language (DSL), as the input code has to be manually decomposed, array accesses have to be manually resolved into multiplexer structures, and the compiled circuits are left unoptimized.…”

Section: Introductionmentioning

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

View full text Add to dashboard Cite

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.

show abstract

Section: Compilation For Mpcmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

HyCC

Büscher

Demmler

Katzenbeisser

et al. 2018

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

View full text Add to dashboard Cite

show abstract

“…Recently, a new framework for compiling two-party protocols called EzPC [29] was presented. EzPC uses ABY as its cryptographic back-end: a simple and easy-to-use imperative programming language is compiled to ABY input.…”

Section: Related Workmentioning

confidence: 99%

“…The current version of Chameleon does not provide automatic protocol selection. However, the methods of [29,51,73] can be applied in future work in order to automatically partition Chameleon programs.…”

Section: Related Workmentioning

confidence: 99%

Chameleon

Riazi

Weinert

Tkachenko

et al. 2018

Proceedings of the 2018 on Asia Conference on Computer and Communications Security

293

View full text Add to dashboard Cite

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 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.

show abstract

“…One example use of MPC is private set intersection (PSI): the x i could be individuals' personal interests, and the function f computes their intersection, revealing which interests the group has in common, but not any interests that they don't. MPC has also been used for auctions [18], detecting tax fraud [16], managing supply chains [33], privacy preserving statistical analysis [31], and more recently for machine learning tasks [19,21,30,38,44].…”

Section: Introductionmentioning

confidence: 99%

$$\textsc {Wys}^\star $$ : A DSL for Verified Secure Multi-party Computations

Rastogi

Swamy

Hicks

2019

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

Secure multi-party computation (MPC) enables a set of mutually distrusting parties to cooperatively compute, using a cryptographic protocol, a function over their private data. This paper presents Wys , a new domain-specific language (DSL) for writing mixed-mode MPCs. Wys is an embedded DSL hosted in F , a verification-oriented, effectful programming language. Wys source programs are essentially F programs written in a custom MPC effect, meaning that the programmers can use F 's logic to verify the correctness and security properties of their programs. To reason about the distributed runtime semantics of these programs, we formalize a deep embedding of Wys , also in F . We mechanize the necessary metatheory to prove that the properties verified for the Wys source programs carry over to the distributed, multi-party semantics. Finally, we use F 's extraction to extract an interpreter that we have proved matches this semantics, yielding a partially verified implementation. Wys is the first DSL to enable formal verification of MPC programs. We have implemented several MPC protocols in Wys , including private set intersection, joint median, and an MPC-based card dealing application, and have verified their correctness and security.

show abstract

EzPC: Programmable and Efficient Secure Two-Party Computation for Machine Learning

Cited by 92 publications

References 48 publications

HyCC

HyCC

Chameleon

$$\textsc {Wys}^\star $$ : A DSL for Verified Secure Multi-party Computations

Contact Info

Product

Resources

About