In privacy-preserving machine learning and many other applications, the involved parties want to obtain the computation result without revealing their private inputs. Secure computation aims to solve this problem, but current secure computation protocols often fail to provide efficient solutions due to large communication, especially in a real-life Internet network where the bandwidth and the delay can be unsatisfying. Assuming the existence of a trusted hardware component that is resilient to side-channel attacks and will faithfully compute a pre-agreed program, secure computation can be realized by each party sending its input to the hardware and receiving the execution result back. However, a recent work of Luet al. (ESORICS’21) points out that the hardware components can’t be fully trusted. In this work, we improve the semi-trusted hardware model of Luet al., and we propose secure computation protocols with low communication in the new model. We observe that the ESORICS’21 two-party computation protocol have some security flaws; in this work, we fix them and improve its online efficiency. Moreover, we propose an efficient constant-round secure multi-party computation protocol which has a communication cost of (n– 1)λ + 2(n– 1)ℓ bits, wherenis the number of the parties, λ is the security parameter and ℓ is the input/output size. The computation cost of our multi-party protocol is also much smaller than current best-known constant-round protocols.
In privacy-preserving machine learning and many other applications, the involved parties want to obtain the computation result without revealing their private inputs. Secure computation aims to solve this problem, but current secure computation protocols often fail to provide efficient solutions due to large communication, especially in a real-life Internet network where the bandwidth and the delay can be unsatisfying. Assuming the existence of a trusted hardware component that is resilient to side-channel attacks and will faithfully compute a pre-agreed program, secure computation can be realized by each party sending its input to the hardware and receiving the execution result back. However, a recent work of Luet al. (ESORICS’21) points out that the hardware components can’t be fully trusted. In this work, we improve the semi-trusted hardware model of Luet al., and we propose secure computation protocols with low communication in the new model. We observe that the ESORICS’21 two-party computation protocol have some security flaws; in this work, we fix them and improve its online efficiency. Moreover, we propose an efficient constant-round secure multi-party computation protocol which has a communication cost of (n– 1)λ + 2(n– 1)ℓ bits, wherenis the number of the parties, λ is the security parameter and ℓ is the input/output size. The computation cost of our multi-party protocol is also much smaller than current best-known constant-round protocols.
Secure multi-party computation (MPC) allows a set of parties to compute a function jointly while keeping their inputs private. Compared with the MPC based on garbled circuits, some recent research results show that MPC based on secret sharing (SS) works at a very high speed. Moreover, SS-based MPC can be easily vectorized and achieve higher throughput. In SS-based MPC, however, we need many communication rounds for computing concrete protocols like equality check, less-than comparison, etc. This property is not suited for large-latency environments like the Internet (or WAN). In this paper, we construct semi-honest secure communication-efficient two-party protocols. The core technique is Beaver triple extension, which is a new tool for treating multi-fan-in gates, and we also show how to use it efficiently. We mainly focus on reducing the number of communication rounds, and our protocols also succeed in reducing the number of communication bits (in most cases). As an example, we propose a less-than comparison protocol (under practical parameters) with three communication rounds. This is less than a half compared with the previous work. Moreover, the number of communication bits is also 38.4% fewer. As a result, total online execution time is 56.1% shorter than the previous work adopting the same settings. Although the computation costs of our protocols are more expensive than those of previous work, we confirm via experiments that such a disadvantage has small effects on the whole online performance in the typical WAN environments.
Secure Multi-Party Computation (MPC) with small number of parties is an interesting area of research, primarily due to its ability to model most real-life MPC applications and the simplicity and efficiency of the resulting protocols. In this work, we present efficient, constant-round 3-party (3PC) and 4-party (4PC) protocols in the honest-majority setting that achieve strong security notions of fairness (corrupted parties receive their output only if all honest parties receive output) and guaranteed output delivery (corrupted parties cannot prevent honest parties from receiving their output). Being constant-round, our constructions are suitable for Internet-like high-latency networks and are built from garbled circuits (GC). Assuming the minimal model of pairwise-private channels, we present two protocols that involve computation and communication of a single GC-- (a) a 4-round 3PC with fairness, (b) a 5-round 4PC with guaranteed output delivery. Empirically, our protocols are on par with the best known 3PC protocol of Mohassel et al. [CCS 2015] that only achieves security with selective abort, in terms of the computation time, LAN runtime, WAN runtime and communication cost. In fact, our 4PC outperforms the 3PC of Mohassel et al. significantly in terms of per-party computation and communication cost. With an extra GC, we improve the round complexity of our 4PC to four rounds. The only 4PC in our setting, given by Ishai et al. [CRYPTO 2015], involves 12 GCs. Assuming an additional broadcast channel, we present a 5-round 3PC with guaranteed output delivery that involves computation and communication of a single GC. A broadcast channel is inevitable in this setting for achieving guaranteed output delivery, owing to an impossibility result in the literature. The overall broadcast communication of our protocol is nominal and most importantly, is independent of the circuit size. This protocol too induces a nominal overhead compared to the protocol of Mohassel et al.
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.
In the setting of secure multiparty computation, a set of parties with private inputs wish to compute some function of their inputs without revealing anything but their output. Over the last decade, the efficiency of secure two-party computation has advanced in leaps and bounds, with speedups of some orders of magnitude, making it fast enough to be of use in practice. In contrast, progress on the case of multiparty computation (with more than two parties) has been much slower, with very little work being done. Currently, the only implemented efficient multiparty protocol has many rounds of communication (linear in the depth of the circuit being computed) and thus is not suited for Internet-like settings where latency is not very low. In this paper, we construct highly efficient constant-round protocols for the setting of multiparty computation for semi-honest adversaries. Our protocols work by constructing a multiparty garbled circuit, as proposed in BMR (Beaver et al., STOC 1990). Our first protocol uses oblivious transfer and constitutes the first concretely-efficient constant-round multiparty protocol for the case of no honest majority. Our second protocol uses BGW, and is significantly more efficient than the FairplayMP protocol (Ben-David et al., CCS 2008) that also uses BGW.
The goal of secure two-party computation is to enable two parties to cooperatively evaluate a function that takes private data from both parties as input without exposing any of the private data. At the end of the computation, the participants learn nothing more than the output of the function. The two-party secure computation systems have three properties: (1) the application involves inputs from two independent parties; (2) each party wants to keep its own data secret; and (3) the participants agree to reveal the output of the computation. That is, the result itself does not imply too much information about either party's private input. Informally, the security requirements are that nothing is learned from the protocol other than the output (privacy), and that the output is distributed according to the prescribed functionality (correctness). The threat models in the two-party computation assume the presence of three different types of adversaries: 1) Semi honest, 2) Malicious and 3) Covert.
ulti-Party Computation (MPC) protocols allow a set of mutually distrustful parties to securely compute on their joint private inputs, maintaining the privacy of those.Since its early conception in the eighties, a plethora of research as well as some real world deployments have taken place, demonstrating the relevance of such an interesting mathematical problem.Nevertheless, most of the research and all deployments have focused on scenarios where both the number of parties and network latency are very low.This thesis lays out the first steps towards practical deployments of large-scale MPC, where many parties may be involved and they might only be connected through a high-latency network such as the Internet.Both theoretical and empirical evidence shows that non-constant-round protocols cannot perform well on slow networks for many functions.In order to overcome this issue, all the works in this thesis include concretely efficient constant-round protocols based on multi-party garbled circuits.Chapter 3 and Chapter 4 deal with the very strong setting where an active adversary corrupts all but one parties.The former achieves such goal by using homomorphic encryption for low-depth circuits.The latter, based on subsequent work, improves the theoretical understanding of the problem and utilises oblivious transfer to improve efficiency.In Chapter 5 we provide both constant and non-constant round protocols that can handle large numbers of parties more efficiently.We leverage our performance improvements by relaxing the setting where all but one parties are corrupted to one where a small minority of honest participants can be assumed.Concretely, such change allows to distribute secret key material so that each party only holds a 'short' part of the key.Security is then based on the concatenation of all honest parties' keys rather than on each party's individual key, improving both the communication and computation complexities.iii researcher.They would have not been the same without the support of family, friends and colleagues.I would like to start by thanking Nigel Smart for giving me this opportunity, being my advisor and introducing me to others in the community.Martijn Stam