Garbling, Stacked and Staggered: Faster k-out-of-n Garbled Function Evaluation

Stacked Garbling (SGC) is a Garbled Circuit (GC) improvement that efficiently and securely evaluates programs with conditional branching. SGC reduces bandwidth consumption such that communication is proportional to the size of the single longest program execution path, rather than to the size of the entire program. Crucially, the parties expend increased computational effort compared to classic GC. Motivated by procuring a subset in a menu of computational services or tasks, we consider GC evaluation of k-out-of-n branches, whose indices are known (or eventually revealed) to the GC evaluator E. Our stack-and-stagger technique amortizes GC computation in this setting. We retain the communication advantage of SGC, while significantly improving computation and wall-clock time. Namely, each GC party garbles (or evaluates) the total of n branches, a significant improvement over the O(nk) garblings/evaluations needed by standard SGC. We present our construction as a garbling scheme. Our technique brings significant overall performance improvement in various settings, including those typically considered in the literature: e.g. on a 1Gbps LAN we evaluate 16-out-of-128 functions ~7.68x faster than standard stacked garbling

MOTIF: (Almost) Free Branching in GMW via Vector-Scalar Multiplication

MPC functionalities are increasingly specified in high-level languages, where control-flow constructions such as conditional statements are extensively used. Today, concretely efficient MPC protocols are circuit-based and must evaluate all conditional branches at high cost to hide the taken branch. The Goldreich-Micali-Wigderson, or GMW, protocol is a foundational circuit-based technique that realizes MPC for p players and is secure against up to p - 1 semi-honest corruptions. While GMW requires communication rounds proportional to the computed circuit’s depth, it is effective in many natural settings. Our main contribution is MOTIF (Minimizing OTs for IFs), a novel GMW extension that evaluates conditional branches almost for free by amortizing Oblivious Transfers (OTs) across branches. That is, we simultaneously evaluate multiple independent AND gates, one gate from each mutually exclusive branch, by representing them as a single cheap vector-scalar multiplication (VS) gate. For 2PC with b branches, we simultaneously evaluate up to b AND gates using only two 1-out-of-2 OTs of b-bit secrets. This is a factor ~b improvement over the state-of-the-art 2b 1-out-of-2 OTs of 1-bit secrets. Our factor b improvement generalizes to the multiparty setting as well: b AND gates consume only p(p - 1) 1-out-of-2 OTs of b-bit secrets. We implemented our approach and report its performance. For 2PC and a circuit with 16 branches, each comparing two length-65000 bitstrings, MOTIF outperforms standard GMW in terms of communication by ~9.4x. Total wall-clock time is improved by 4.1 - 9.2x depending on network settings. Our work is in the semi-honest model, tolerating all-but-one corruptions

Masked Triples: Amortizing Multiplication Triples across Conditionals

A classic approach to MPC uses preprocessed multiplication triples to evaluate arbitrary Boolean circuits. If the target circuit features conditional branching, e.g. as the result of a IF program statement, then triples are wasted: one triple is consumed per AND gate, even if the output of the gate is entirely discarded by the circuit\u27s conditional behavior. In this work, we show that multiplication triples can be re-used across conditional branches. For a circuit with $b$ branches, each having $n$ AND gates, we need only a total of $n$ triples, rather than the typically required $b\cdot n$. Because preprocessing triples is often the most expensive step in protocols that use them, this significantly improves performance. Prior work similarly amortized oblivious transfers across branches in the classic GMW protocol (Heath et al., Asiacrypt 2020, [HKP20]). In addition to demonstrating conditional improvements are possible for a different class of protocols, we also concretely improve over [HKP20]: their maximum improvement is bounded by the topology of the circuit. Our protocol yields improvement independent of topology: we need triples proportional to the size of the program\u27s longest execution path, regardless of the structure of the program branches. We implemented our approach in C++. Our experiments show that we significantly improve over a naive protocol and over prior work: for a circuit with $16$ branches and in terms of total communication, we improved over naive by $12\times$ and over [HKP20] by an average of $2.6\times$. Our protocol is secure against the semi-honest corruption of $p-1$ parties

Communication-Efficient Secure Logistic Regression

We present a new construction for secure logistic regression training, which enables two parties to train a model on private secret-shared data. Our goal is to minimize online communication and round complexity, while still allowing for an efficient offline phase. As part of our construction we develop many building blocks of independent interest. These include a new approximation technique for the sigmoid function, which results in a secure protocol with better communication; secure spline evaluation and secure powers computation protocols for fixed-point values; and a new comparison protocol that optimizes online communication. We also present a new two-party protocol for generating keys for distributed point functions (DPFs) over arithmetic sharing, where previous constructions do this only for Boolean outputs. We implement our protocol in an end-to-end system and benchmark its efficiency. We can securely evaluate a sigmoid in $18$ ms online time and $0.5$ KB of online communication. Our system can train a model over a database with $70,000$ samples and $15$ features with online communication of $208.09$ MB and online time of $2.24$ hours at the cost of $6.11$c over WAN. Our benchmarks demonstrate that we reduce online communication over state of the art by $\approx 10 \times$ for sigmoid and $\approx38\times$ for logistic regression training