Minimising Communication in Honest-Majority MPC by Batchwise Multiplication Verification

In this paper, we present two new and very communication-efficient protocols for maliciously secure multi-party computation over fields in the honest-majority setting with abort. Our first protocol improves a recent protocol by Lindell and Nof. Using the so far overlooked tool of batchwise multiplication verification, we speed up their technique for checking correctness of multiplications (with some other improvements), reducing communication by 2x to 7x. In particular, in the 3PC setting, each party sends only two field elements per multiplication. We also show how to achieve fairness, which Lindell and Nof left as an open problem. Our second protocol again applies batchwise multiplication verification, this time to perform 3PC by letting two parties perform the SPDZ protocol using triples generated by a third party and verified batchwise. In this protocol, each party sends only 4/3 field elements during the online phase and 5/3 field elements during the preprocessing phase

SWIFT: Super-fast and Robust Privacy-Preserving Machine Learning

Performing machine learning (ML) computation on private data while maintaining data privacy, aka Privacy-preserving Machine Learning~(PPML), is an emergent field of research. Recently, PPML has seen a visible shift towards the adoption of the Secure Outsourced Computation~(SOC) paradigm due to the heavy computation that it entails. In the SOC paradigm, computation is outsourced to a set of powerful and specially equipped servers that provide service on a pay-per-use basis. In this work, we propose SWIFT, a robust PPML framework for a range of ML algorithms in SOC setting, that guarantees output delivery to the users irrespective of any adversarial behaviour. Robustness, a highly desirable feature, evokes user participation without the fear of denial of service. At the heart of our framework lies a highly-efficient, maliciously-secure, three-party computation (3PC) over rings that provides guaranteed output delivery (GOD) in the honest-majority setting. To the best of our knowledge, SWIFT is the first robust and efficient PPML framework in the 3PC setting. SWIFT is as fast as (and is strictly better in some cases than) the best-known 3PC framework BLAZE (Patra et al. NDSS'20), which only achieves fairness. We extend our 3PC framework for four parties (4PC). In this regime, SWIFT is as fast as the best known fair 4PC framework Trident (Chaudhari et al. NDSS'20) and twice faster than the best-known robust 4PC framework FLASH (Byali et al. PETS'20). We demonstrate our framework's practical relevance by benchmarking popular ML algorithms such as Logistic Regression and deep Neural Networks such as VGG16 and LeNet, both over a 64-bit ring in a WAN setting. For deep NN, our results testify to our claims that we provide improved security guarantee while incurring no additional overhead for 3PC and obtaining 2x improvement for 4PC.Comment: This article is the full and extended version of an article to appear in USENIX Security 202

Two-Thirds Honest-Majority MPC for Malicious Adversaries at Almost the Cost of Semi-Honest

Secure multiparty computation (MPC) enables a set of parties to securely carry out a joint computation of their private inputs without revealing anything but the output. Protocols for semi-honest adversaries guarantee security as long as the corrupted parties run the specified protocol and ensure that nothing is leaked in the transcript. In contrast, protocols for malicious adversaries guarantee security in the presence of arbitrary adversaries who can run any attack strategy. Security for malicious adversaries is typically what is needed in practice (and is always preferred), but comes at a significant cost. In this paper, we present the first protocol for a two-thirds honest majority that achieves security in the presence of malicious adversaries at essentially the exact same cost as the best known protocols for semi-honest adversaries. Our construction is not a general transformation and thus it is possible that better semi-honest protocols will be constructed which do not support our transformation. Nevertheless, for the current state-of-the-art for many parties (based on Shamir sharing), our protocol invokes the best semi-honest multiplication protocol exactly once per multiplication gate (plus some additional local computation that is negligible to the overall cost). Concretely, the best version of our protocol requires each party to send on average of just $2\frac23$ elements per multiplication gate (when the number of multiplication gates is at least the number of parties). This is four times faster than the previous-best protocol of Barak et al. (ACM CCS 2018) for small fields, and twice as fast as the previous-best protocol of Chida et al. (CRYPTO 2018) for large fields

Malicious Security Comes Free in Honest-Majority MPC

We study the communication complexity of unconditionally secure MPC over point-to-point channels for corruption threshold t < n/2. We ask the question: is it possible to achieve security-with-abort with the same concrete cost as the best-known semi-honest MPC protocol? While a number of works have focused on improving the concrete efficiency in this setting, the answer to the above question has remained elusive until now. We resolve the above question in the affirmative by providing a secure-with-abort MPC protocol with the same cost per gate as the best-known semi-honest protocol. Concretely, our protocol only needs 5.5 field elements per multiplication gate per party which matches (and even improves upon) the corresponding cost of the best known protocol in the semi-honest setting by Damgard and Nielsen. Previously best-known maliciously secure (with abort) protocols require 12 field elements. An additional feature of our protocol is its conceptual simplicity

BLAZE: Blazing Fast Privacy-Preserving Machine Learning

Machine learning tools have illustrated their potential in many significant sectors such as healthcare and finance, to aide in deriving useful inferences. The sensitive and confidential nature of the data, in such sectors, raise natural concerns for the privacy of data. This motivated the area of Privacy-preserving Machine Learning (PPML) where privacy of the data is guaranteed. Typically, ML techniques require large computing power, which leads clients with limited infrastructure to rely on the method of Secure Outsourced Computation (SOC). In SOC setting, the computation is outsourced to a set of specialized and powerful cloud servers and the service is availed on a pay-per-use basis. In this work, we explore PPML techniques in the SOC setting for widely used ML algorithms-- Linear Regression, Logistic Regression, and Neural Networks. We propose BLAZE, a blazing fast PPML framework in the three server setting tolerating one malicious corruption over a ring (\Z{\ell}). BLAZE achieves the stronger security guarantee of fairness (all honest servers get the output whenever the corrupt server obtains the same). Leveraging an input-independent preprocessing phase, BLAZE has a fast input-dependent online phase relying on efficient PPML primitives such as: (i) A dot product protocol for which the communication in the online phase is independent of the vector size, the first of its kind in the three server setting; (ii) A method for truncation that shuns evaluating expensive circuit for Ripple Carry Adders (RCA) and achieves a constant round complexity. This improves over the truncation method of ABY3 (Mohassel et al., CCS 2018) that uses RCA and consumes a round complexity that is of the order of the depth of RCA. An extensive benchmarking of BLAZE for the aforementioned ML algorithms over a 64-bit ring in both WAN and LAN settings shows massive improvements over ABY3.Comment: The Network and Distributed System Security Symposium (NDSS) 202

Experimenting with Collaborative zk-SNARKs: Zero-Knowledge Proofs for Distributed Secrets

A zk-SNARK is a powerful cryptographic primitive that provides a succinct and efficiently checkable argument that the prover has a witness to a public NP statement, without revealing the witness. However, in their native form, zk-SNARKs only apply to a secret witness held by a single party. In practice, a collection of parties often need to prove a statement where the secret witness is distributed or shared among them. We implement and experiment with *collaborative zk-SNARKs*: proofs over the secrets of multiple, mutually distrusting parties. We construct these by lifting conventional zk-SNARKs into secure protocols among $N$ provers to jointly produce a single proof over the distributed witness. We optimize the proof generation algorithm in pairing-based zk-SNARKs so that algebraic techniques for multiparty computation (MPC) yield efficient proof generation protocols. For some zk-SNARKs, optimization is more challenging. This suggests MPC friendliness as an additional criterion for evaluating zk-SNARKs. We implement three collaborative proofs and evaluate the concrete cost of proof generation. We find that over a 3Gb/s link, security against a malicious minority of provers can be achieved with *approximately the same runtime* as a single prover. Security against $N-1$ malicious provers requires only a $2\times$ slowdown. This efficiency is unusual since most computations slow down by orders of magnitude when securely distributed. This efficiency means that most applications that can tolerate the cost of a single-prover proof should also be able to tolerate the cost of a collaborative proof

Asymptotically Good Multiplicative LSSS over Galois Rings and Applications to MPC over Z/ pkZ

We study information-theoretic multiparty computation (MPC) protocols over rings Z/ pkZ that have good asymptotic communication complexity for a large number of players. An important ingredient for such protocols is arithmetic secret sharing, i.e., linear secret-sharing schemes with multiplicative properties. The standard way to obtain these over fields is with a family of linear codes C, such that C, C⊥ and C2 are asymptotically good (strongly multiplicative). For our purposes here it suffices if the square code C2 is not the whole space, i.e., has codimension at least 1 (multiplicative). Our approach is to lift such a family of codes defined over a finite field F to a Galois ring, which is a local ring that has F as its residue field and that contains Z/ pkZ as a subring, and thus enables arithmetic that is compatible with both structures. Although arbitrary lifts preserve the distance and dual distance of a code, as we demonstrate with a counterexample, the multiplicative property is not preserved. We work around this issue by showing a dedicated lift that preserves self-orthogonality (as well as distance and dual distance), for p≥ 3. Self-orthogonal codes are multiplicative, therefore we can use existing results of asymptotically good self-dual codes over fields to obtain arithmetic secret sharing over Galois rings. For p= 2 we obtain multiplicativity by using existing techniques of secret-sharing using both C and C⊥, incurring a constant overhead. As a result, we obtain asymptotically good arithmetic secret-sharing schemes over Galois rings. With these schemes in hand, we extend existing field-based MPC protocols to obtain MPC over Z/ pkZ, in the setting of a submaximal adversary corrupting less than a fraction 1 / 2 - ε of the players, where ε&gt; 0 is arbitrarily small. We consider 3 different corruption models. For passive and active security with abort, our protocols communicate O(n) bits per multiplication. For full security with guaranteed output delivery we use a preprocessing model and get O(n) bits per multiplication in the online phase and O(nlog n) bits per multiplication in the offline phase. Thus, we obtain true linear bit complexities, without the common assumption that the ring size depends on the number of players

Practical Fully Secure Three-Party Computation via Sublinear Distributed Zero-Knowledge Proofs

Secure multiparty computation enables a set of parties to securely carry out a joint computation on their private inputs without revealing anything but the output. A particularly motivated setting is that of three parties with a single corruption (hereafter denoted 3PC). This 3PC setting is particularly appealing for two main reasons: (1) it admits more efficient MPC protocols than in other standard settings; (2) it allows in principle to achieve full security (and fairness). Highly efficient protocols exist within this setting with security against a semi-honest adversary; however, a significant gap remains between these and protocols with stronger security against a malicious adversary. In this paper, we narrow this gap within concretely efficient protocols. More explicitly, we have the following contributions: * Concretely Efficient Malicious 3PC. We present an optimized 3PC protocol for arithmetic circuits over rings with (amortized) communication of 1 ring element per multiplication gate per party, matching the best semi-honest protocols. The protocol applies also to Boolean circuits, significantly improving over previous protocols even for small circuits. Our protocol builds on recent techniques of Boneh et al.\ (Crypto 2019) for sublinear zero-knowledge proofs on distributed data, together with an efficient semi-honest protocol based on replicated secret sharing (Araki et al., CCS 2016). We present a concrete analysis of communication and computation costs, including several optimizations. For example, for 40-bit statistical security, and Boolean circuit with a million (nonlinear) gates, the overhead on top of the semi-honest protocol can involve less than 0.5KB of communication {\em for the entire circuit}, while the computational overhead is dominated by roughly 30 multiplications per gate in the field $F_{2^{47}}$. In addition, we implemented and benchmarked the protocol for varied circuit sizes. * Full Security. We augment the 3PC protocol to further provide full security (with guaranteed output delivery) while maintaining amortized 1 ring element communication per party per multiplication gate, and with hardly any impact on concrete efficiency. This is contrasted with the best previous 3PC protocols from the literature, which allow a corrupt party to mount a denial-of-service attack without being detected

ASTRA: High Throughput 3PC over Rings with Application to Secure Prediction

The concrete efficiency of secure computation has been the focus of many recent works. In this work, we present concretely-efficient protocols for secure $3$-party computation (3PC) over a ring of integers modulo $2^{\ell}$ tolerating one corruption, both with semi-honest and malicious security. Owing to the fact that computation over ring emulates computation over the real-world system architectures, secure computation over ring has gained momentum of late. Cast in the offline-online paradigm, our constructions present the most efficient online phase in concrete terms. In the semi-honest setting, our protocol requires communication of $2$ ring elements per multiplication gate during the {\it online} phase, attaining a per-party cost of {\em less than one element}. This is achieved for the first time in the regime of 3PC. In the {\it malicious} setting, our protocol requires communication of $4$ elements per multiplication gate during the online phase, beating the state-of-the-art protocol by $5$ elements. Realized with both the security notions of selective abort and fairness, the malicious protocol with fairness involves slightly more communication than its counterpart with abort security for the output gates {\em alone}. We apply our techniques from $3$PC in the regime of secure server-aided machine-learning (ML) inference for a range of prediction functions-- linear regression, linear SVM regression, logistic regression, and linear SVM classification. Our setting considers a model-owner with trained model parameters and a client with a query, with the latter willing to learn the prediction of her query based on the model parameters of the former. The inputs and computation are outsourced to a set of three non-colluding servers. Our constructions catering to both semi-honest and the malicious world, invariably perform better than the existing constructions.Comment: This article is the full and extended version of an article appeared in ACM CCSW 201

Trident: Efficient 4PC Framework for Privacy Preserving Machine Learning

Machine learning has started to be deployed in fields such as healthcare and finance, which propelled the need for and growth of privacy-preserving machine learning (PPML). We propose an actively secure four-party protocol (4PC), and a framework for PPML, showcasing its applications on four of the most widely-known machine learning algorithms -- Linear Regression, Logistic Regression, Neural Networks, and Convolutional Neural Networks. Our 4PC protocol tolerating at most one malicious corruption is practically efficient as compared to the existing works. We use the protocol to build an efficient mixed-world framework (Trident) to switch between the Arithmetic, Boolean, and Garbled worlds. Our framework operates in the offline-online paradigm over rings and is instantiated in an outsourced setting for machine learning. Also, we propose conversions especially relevant to privacy-preserving machine learning. The highlights of our framework include using a minimal number of expensive circuits overall as compared to ABY3. This can be seen in our technique for truncation, which does not affect the online cost of multiplication and removes the need for any circuits in the offline phase. Our B2A conversion has an improvement of $\mathbf{7} \times$ in rounds and $\mathbf{18} \times$ in the communication complexity. The practicality of our framework is argued through improvements in the benchmarking of the aforementioned algorithms when compared with ABY3. All the protocols are implemented over a 64-bit ring in both LAN and WAN settings. Our improvements go up to $\mathbf{187} \times$ for the training phase and $\mathbf{158} \times$ for the prediction phase when observed over LAN and WAN.Comment: This work appeared at the 26th Annual Network and Distributed System Security Symposium (NDSS) 2020. Update: An improved version of this framework is available at arXiv:2106.0285