Secure Floating-Point Training

Secure 2-party computation (2PC) of floating-point arithmetic is improving in performance and recent work runs deep learning algorithms with it, while being as numerically precise as commonly used machine learning (ML) frameworks like PyTorch. We find that the existing 2PC libraries for floating-point support generic computations and lack specialized support for ML training. Hence, their latency and communication costs for compound operations (e.g., dot products) are high. We provide novel specialized 2PC protocols for compound operations and prove their precision using numerical analysis. Our implementation BEACON outperforms state-of-the-art libraries for 2PC of floating-point by over $6\times$

Fast Secure Matrix Multiplications over Ring-Based Homomorphic Encryption

Secure matrix computation is one of the most fundamental and useful operations for statistical analysis and machine learning with protecting the confidentiality of input data. Secure computation can be achieved by homomorphic encryption, supporting meaningful operations over encrypted data. HElib is a software library that implements the Brakerski-Gentry-Vaikuntanathan (BGV) homomorphic scheme, in which secure matrix-vector multiplication is proposed for operating matrices. Recently, Duong et al. (Tatra Mt. Publ. 2016) proposed a new method for secure single matrix multiplication over a ring-LWE-based scheme. In this paper, we generalize Duong et al.\u27s method for secure multiple matrix multiplications over the BGV scheme. We also implement our method using HElib and show that our method is much faster than the matrix-vector multiplication in HElib for secure matrix multiplications

ZEBRA: SNARK-based Anonymous Credentials for Practical, Private and Accountable On-chain Access Control

Restricting access to certified users is not only desirable for many blockchain applications, it is also legally mandated for decentralized finance (DeFi) applications to counter malicious actors. Existing solutions, however, are either (i) non-private, i.e., they reveal the link between users and their wallets to the authority granting credentials, or (ii) they introduce additional trust assumptions by relying on a decentralized oracle to verify anonymous credentials (ACs). To remove additional trust in the latter approach, we propose verifying credentials on-chain in this work. We find that this approach has impractical costs with prior AC schemes, and propose a new AC scheme ZEBRA that crucially relies on zkSNARKs to provide efficient on-chain verification for the first time. In addition to the standard unlinkability property that provides privacy for users, ZEBRA also supports auditability, revocation, traceability, and theft detection, which adds accountability for malicious users and convenience for honest users to our access control solution. Even with these properties, ZEBRA reduces the gas cost incurred on the Ethereum Virtual Machine (EVM) by 14.3x when compared to Coconut [NDSS 2019], the state-of-the-art AC scheme for blockchains that only provides unlinkability. This improvement translates to a reduction in transaction fees from 176 USD to 12 USD on Ethereum in May 2023. Since 12 USD is still high for most applications, ZEBRA further drives down credential verification costs through batched verification. For a batch of 512 layer-1 and layer-2 wallets, the transaction fee on Ethereum is reduced to just 0.44 USD and 0.02 USD, respectively, which is comparable to the minimum transaction costs on Ethereum

SecFloat: Accurate Floating-Point meets Secure 2-Party Computation

We build a library SecFloat for secure 2-party computation (2PC) of 32-bit single-precision floating-point operations and math functions. The existing functionalities used in cryptographic works are imprecise and the precise functionalities used in standard libraries are not crypto-friendly, i.e., they use operations that are cheap on CPUs but have exorbitant cost in 2PC. SecFloat bridges this gap with its novel crypto-friendly precise functionalities. Compared to the prior cryptographic libraries, SecFloat is up to six orders of magnitude more precise and up to two orders of magnitude more efficient. Furthermore, against a precise 2PC baseline, SecFloat is three orders of magnitude more efficient. The high precision of SecFloat leads to the first accurate implementation of secure inference. All prior works on secure inference of deep neural networks rely on ad hoc float-to-fixed converters. We evaluate a model where the fixed-point approximations used in privacy-preserving machine learning completely fail and floating-point is necessary. Thus, emphasizing the need for libraries like SecFloat

Linear-Complexity Private Function Evaluation is Practical - Performance measurement data

Performance measurement data for the paper 'Linear-Complexity Private Function Evaluation is Practical