Lookup table (LUT) is an important cryptography primitive, widely used in secure applications such as private set intersection, boolean circuit evaluation, and privacy-preserving machine learning. However, existing LUT constructions suffer from either high overhead or limited functionality. In this paper, we propose ROTL, a secure two-party protocol for arithmetic LUT evaluation. Compared with SP-LUT (the state-of-the-art arithmetic LUT presented at NDSS '17), it achieves up to 3.3x speedup and 10.5x communication reduction in overall (preprocessing + online) and 21x speedup and 60x communication reduction in terms of the online phase. At the heart of ROTL is a novel protocol for secret-sharing rotation, which allows two parties to generate additive secret shares of the rotated table without revealing the rotation offset. We believe this protocol is of independent interest. Based on ROTL, we design a novel secure comparison protocol; compared with the state-of-the-art (USENIX '22), it achieves a 5x runtime speedup and 2.5x communication reduction in the online performance. To support boolean secret sharing, we further provide an optimization (named FLUTE+) for FLUTE (the state-of-the-art boolean LUT presented at Oakland '23). For a boolean LUT with table size n and elements bit-width l, we reduce FLUTE's computation complexity from O(n^2 l) to O(n log n + n l) and shift O(n log n) computation to the preprocessing phase without introducing communication overhead. As a result, FLUTE+ achieves up to 5x speedup in terms of overall (preprocessing and online) and over 600x speedup in terms of the online phase compared with FLUTE. The communication cost of FLUTE+ is exactly the same as FLUTE's in both the preprocessing phase and the online phase.
Lookup table (LUT) is an important cryptography primitive, widely used in secure applications such as private set intersection, boolean circuit evaluation, and privacy-preserving machine learning. However, existing LUT constructions suffer from either high overhead or limited functionality. In this paper, we propose ROTL, a secure two-party protocol for arithmetic LUT evaluation. Compared with SP-LUT (the state-of-the-art arithmetic LUT presented at NDSS '17), it achieves up to 3.3x speedup and 10.5x communication reduction in overall (preprocessing + online) and 21x speedup and 60x communication reduction in terms of the online phase. At the heart of ROTL is a novel protocol for secret-sharing rotation, which allows two parties to generate additive secret shares of the rotated table without revealing the rotation offset. We believe this protocol is of independent interest. Based on ROTL, we design a novel secure comparison protocol; compared with the state-of-the-art (USENIX '22), it achieves a 5x runtime speedup and 2.5x communication reduction in the online performance. To support boolean secret sharing, we further provide an optimization (named FLUTE+) for FLUTE (the state-of-the-art boolean LUT presented at Oakland '23). For a boolean LUT with table size n and elements bit-width l, we reduce FLUTE's computation complexity from O(n^2 l) to O(n log n + n l) and shift O(n log n) computation to the preprocessing phase without introducing communication overhead. As a result, FLUTE+ achieves up to 5x speedup in terms of overall (preprocessing and online) and over 600x speedup in terms of the online phase compared with FLUTE. The communication cost of FLUTE+ is exactly the same as FLUTE's in both the preprocessing phase and the online phase.
A secure interval test protocol is a powerful and useful tool to check whether a shared secret value is within a particular interval without revealing any information about the secret value, and therefore it has important applications in secure multi-party computation, machine learning, privacy-preserving location services, and many other areas. The secure inerval test protocol can be used as a building block in larger or more complex systems. A secure table lookup protocol based on the secure interval test can be used to implement approximate calculations of certain functions. In this work, we propose a square table lookup protocol based on the secure interval test protocol, which improves the efficiency in a certain setting compared to the state-of-the-art secure table lookup protocol proposed by Morita el al. (CANS'19). We reduce the computational complexity of the table lookup protocol fromO(N)toO(N)whereNis the number of elements in the lookup table by changing the dimension of the lookup table from a one-dimensional array to a two-dimensional array, arranging it as a square matrix, and redesigning the procedure of secure table lookup protocol.
Secure multi-party computation (MPC) is a cryptographic tool that enables a set of parties to compute a function jointly while keeping each input secret. Since MPC based on secret sharing (SS) achieves high throughput and works fast, many applications have been developed. However, SS-based MPC requires many communication rounds in general, and this becomes a performance bottleneck in real-world applications under high-latency networks. In this paper, we propose SS-based secure three-party computation with almost no preprocessing based on our new (small-)constant-round fundamental gates, by revisiting a framework in a few previous works where a number of parties are assisted by another party who may partially learn secret information. Instead of ordinary logical gates, our fundamental gate is an efficient Equality, for which the result leaks to the third party, and we develop novel two-round constructions of secure building-block protocols (LessThan Comparison, RightShift, Table LookUp, etc.) from the insecure Equality. To show the practicality of our protocols, we implement a secure exact edit distance protocol for two genome strings. Our experiments show that in some network setting our protocol is about 2 times faster (14 times faster taking preprocessing into consideration) than the state-of-the-art SS-based protocol (Ohata and Nuida, FC 2020).
Secure comparison is a fundamental primitive in multi-party computation, supporting privacy-preserving applications such as machine learning and data analytics. A critical performance bottleneck in comparison protocols is their preprocessing phase, primarily due to the high cost of generating the necessary correlated randomness. Recent frameworks introduce a passive, non-colluding dealer to accelerate preprocessing. However, two key issues still remain. First, existing dealer-assisted approaches treat the dealer as a drop-in replacement for conventional preprocessing without redesigning the comparison protocol to optimize the online phase. Second, most protocols are specialized for particular algebraic domains, adversary models, or party configurations, lacking broad generality. In this work, we present the first dealer-assisted n-party LTBits (Less-Than-Bits) and MSB (Most Significant Bit) extraction protocols over both Fp and Z2k, achieving perfect security at the protocol level. By fully exploiting the dealer's capability to generate rich correlated randomness, our Fp construction achieves constant-round online complexity and our Z2k construction achieves O(lognk) rounds with tunable branching factor. All protocols are formulated as black-box constructions via an extended ABB model, ensuring portability across MPC backends and adversary models. Experimental results demonstrate 1.79× to 19.4× speedups over state-of-the-art MPC frameworks, highlighting the practicality of our protocols for comparison-intensive MPC applications.
The advantage of mixed-protocol multi-party secure computation frameworks lies in their ability to utilize different sharing types optimally for diverse tasks. A key module in these frameworks are Boolean-to-arithmetic secret sharing conversion protocols, which transfer a secret value from Boolean secret sharing to arithmetic secret sharing. This conversion process can either take in the Boolean secret sharing of a bit or a secret binary string. In this work, we suggest the application of an innovative correlated random tuple for this task in the semi-honest three-party (3PC) setting. This tuple provides the basis for building Boolean-to-arithmetic share conversion protocols. Specifically, we propose two such protocols in the semi-honest 3PC setting, the first protocol takes as input the Boolean secret sharing of a bit, and the second protocol takes as input the Boolean secret sharing of a secret binary string. When it comes to concrete efficiency, the first protocol shows superior performance compared to the existing state-of-the-art in ABY3 (CCS ’18). It achieves this by reducing the total required communication from 2ℓ bits per party to 4ℓ/3+1 bits (including 4ℓ/3 bits in the setup phase, and 1 bit in the online phase) per party, while maintaining a single round of optimized communication. On the other hand, the second protocol involves two rounds of online communication and its communication cost is comparable to that of ABY2.0 (USENIX’21) that relies on correlated oblivious transfer.
Secure Multi-party Computation (MPC) allows a set of mutually distrusting parties to jointly evaluate a function on their private inputs while maintaining input privacy. In this work, we improve semi-honest secure two-party computation (2PC) over rings, with a focus on the efficiency of the online phase. We propose an efficient mixed-protocol framework, outperforming the state-of-the-art 2PC framework of ABY. Moreover, we extend our techniques to multi-input multiplication gates without inflating the online communication, i.e., it remains independent of the fan-in. Along the way, we construct efficient protocols for several primitives such as scalar product, matrix multiplication, comparison, maxpool, and equality testing. The online communication of our scalar product is two ring elements irrespective of the vector dimension, which is a feature achieved for the first time in the 2PC literature. The practicality of our new set of protocols is showcased with four applications: i) AES S-box, ii) Circuit-based Private Set Intersection, iii) Biometric Matching, and iv) Privacy-preserving Machine Learning (PPML). Most notably, for PPML, we implement and benchmark training and inference of Logistic Regression and Neural Networks over LAN and WAN networks. For training, we improve online runtime (both for LAN and WAN) over SecureML (Mohassel et al., IEEE S&P '17) in the range 1.5x–6.1x, while for inference, the improvements are in the range of 2.5x–754.3x.
We study the problem of secure two-party computation of arithmetic circuits in the presence of active (malicious) parties. This problem is motivated by privacy-preserving numerical computations, such as ones arising in the context of machine learning training and classification, as well as in threshold cryptographic schemes. In this work, we design, optimize, and implement anactively secure protocol for secure two-party arithmetic computation. A distinctive feature of our protocol is that it can make a fully modular black-box use of any passively secure implementation of oblivious linear function evaluation (OLE). OLE is a commonly used primitive for secure arithmetic computation, analogously to the role of oblivious transfer in secure computation for Boolean circuits. For typical (large but not-too-narrow) circuits, our protocol requires roughly 4 invocations of passively secure OLE per multiplication gate. This significantly improves over the recent TinyOLE protocol (Dottling et al., ACM CCS 2017), which requires 22 invocations of actively secure OLE in general, or 44 invocations of a specific code-based passively secure OLE. Our protocol follows the high level approach of the IPS compiler (Ishai et al., CRYPTO 2008, TCC 2009), optimizing it in several ways. In particular, we adapt optimization ideas that were used in the context of the practical zero-knowledge argument system Ligero (Ames et al., ACM CCS 2017) to the more general setting of secure computation, and explore the possibility of boosting efficiency by employing a leaky passively secure OLE protocol. The latter is motivated by recent (passively secure) lattice-based OLE implementations in which allowing such leakage enables better efficiency. We showcase the performance of our protocol by applying its implementation to several useful instances of secure arithmetic computation. On wide circuits, such as ones computing a fixed function on many different inputs, our protocol is 5x faster and transmits 4x less data than the state-of-the-art Overdrive (Keller et al., Eurocrypt 2018). Our benchmarks include a general passive-to-active OLE compiler, authenticated generation of Beaver triples, and a system for securely outsourcing neural network classification. The latter is the first actively secure implementation of its kind, strengthening the passive security provided by recent related works (Mohassel and Zhang, IEEE SP Juvekar et al., USENIX 2018).