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

MPClan: Protocol Suite for Privacy-Conscious Computations

The growing volumes of data being collected and its analysis to provide better services are creating worries about digital privacy. To address privacy concerns and give practical solutions, the literature has relied on secure multiparty computation. However, recent research has mostly focused on the small-party honest-majority setting of up to four parties, noting efficiency concerns. In this work, we extend the strategies to support a larger number of participants in an honest-majority setting with efficiency at the center stage. Cast in the preprocessing paradigm, our semi-honest protocol improves the online complexity of the decade-old state-of-the-art protocol of Damgård and Nielson (CRYPTO\u2707). In addition to having an improved online communication cost, we can shut down almost half of the parties in the online phase, thereby saving up to 50$\%$ in the system\u27s operational costs. Our maliciously secure protocol also enjoys similar benefits and requires only half of the parties, except for one-time verification, towards the end. To showcase the practicality of the designed protocols, we benchmark popular applications such as deep neural networks, graph neural networks, genome sequence matching, and biometric matching using prototype implementations. Our improved protocols aid in bringing up to 60-80$\%$ savings in monetary cost over prior work

Entrada to Secure Graph Convolutional Networks

Graph convolutional networks (GCNs) are gaining popularity due to their powerful modelling capabilities. However, guaranteeing privacy is an issue when evaluating on inputs that contain users’ sensitive information such as financial transactions, medical records, etc. To address such privacy concerns, we design Entrada, a framework for securely evaluating GCNs that relies on the technique of secure multiparty computation (MPC). For efficiency and accuracy reasons, Entrada builds over the MPC framework of Tetrad (NDSS’22) and enhances the same by providing the necessary primitives. Moreover, Entrada leverages the GraphSC paradigm of Araki et al. (CCS’21) to further enhance efficiency. This entails designing a secure and efficient shuffle protocol specifically in the 4-party setting, which to the best of our knowledge, is done for the first time and may be of independent interest. Through extensive experiments, we showcase that the accuracy of secure GCN evaluated via Entrada is on par with its cleartext counterpart. We also benchmark efficiency of Entrada with respect to the included primitives as well as the framework as a whole. Finally, we showcase Entrada’s practicality by benchmarking GCN-based fraud detection application

PentaGOD: Stepping beyond Traditional GOD with Five Parties

Secure multiparty computation (MPC) is increasingly being used to address privacy issues in various applications. The recent work of Alon et al. (CRYPTO\u2720) identified the shortcomings of traditional MPC and defined a Friends-and-Foes (FaF) security notion to address the same. We showcase the need for FaF security in real-world applications such as dark pools. This subsequently necessitates designing concretely efficient FaF-secure protocols. Towards this, keeping efficiency at the center stage, we design ring-based FaF-secure MPC protocols in the small-party honest-majority setting. Specifically, we provide (1,1)-FaF secure 5 party computation protocols (5PC) that consider one malicious and one semi-honest corruption and constitutes the optimal setting for attaining honest-majority. At the heart of it lies the multiplication protocol that requires a single round of communication with 8 ring elements (amortized). To facilitate having FaF-secure variants for several applications, we design a variety of building blocks optimized for our FaF setting. The practicality of the designed (1,1)-FaF secure 5PC framework is showcased by benchmarking dark pools. In the process, we also improve the efficiency and security of the dark pool protocols over the existing traditionally secure ones. This improvement is witnessed as a gain of up to $62\times$ in throughput compared to the existing ones. Finally, to demonstrate the versatility of our framework, we also benchmark popular deep neural networks

CompactTag: Minimizing Computation Overheads in Actively-Secure MPC for Deep Neural Networks

Secure Multiparty Computation (MPC) protocols enable secure evaluation of a circuit by several parties, even in the presence of an adversary who maliciously corrupts all but one of the parties. These MPC protocols are constructed using the well-known secret-sharing-based paradigm (SPDZ and SPD$\mathbb{Z}_{2^k}$), where the protocols ensure security against a malicious adversary by computing Message Authentication Code (MAC) tags on the input shares and then evaluating the circuit with these input shares and tags. However, this tag computation adds a significant runtime overhead, particularly for machine learning (ML) applications with computationally intensive linear layers, such as convolutions and fully connected layers. To alleviate the tag computation overhead, we introduce CompactTag, a lightweight algorithm for generating MAC tags specifically tailored for linear layers in ML. Linear layer operations in ML, including convolutions, can be transformed into Toeplitz matrix multiplications. For the multiplication of two matrices with dimensions T1 × T2 and T2 × T3 respectively, SPD$\mathbb{Z}_{2^k}$ required O(T1 · T2 · T3) local multiplications for the tag computation. In contrast, CompactTag only requires O(T1 · T2 + T1 · T3 + T2 · T3) local multiplications, resulting in a substantial performance boost for various ML models. We empirically compared our protocol to the SPD$\mathbb{Z}_{2^k}$ protocol for various ML circuits, including ResNet Training-Inference, Transformer Training-Inference, and VGG16 Training-Inference. SPD$\mathbb{Z}_{2^k}$ dedicated around 30% of its online runtime for tag computation. CompactTag speeds up this tag computation bottleneck by up to 23×, resulting in up to 1.47× total online phase runtime speedups for various ML workloads

Shield: Secure Allegation Escrow System with Stronger Guarantees

The rising issues of harassment, exploitation, corruption, and other forms of abuse have led victims to seek comfort by acting in unison against common perpetrators (e.g., #MeToo movement). One way to curb these issues is to install allegation escrow systems that allow victims to report such incidents. The escrows are responsible for identifying victims of a common perpetrator and taking the necessary action to bring justice to them. However, users hesitate to participate in these systems due to the fear of such sensitive reports being leaked to perpetrators, who may further misuse them. Thus, to increase trust in the system, cryptographic solutions are being designed to realize secure allegation escrow (SAE) systems. In the work of Arun et al. (NDSS\u2720), which presents the state-of-the-art solution, we identify attacks that can leak sensitive information and compromise victim privacy. We also report issues present in prior works that were left unidentified. To arrest all these breaches, we put forth an SAE system that prevents the identified attacks and retains the salient features from all prior works. The cryptographic technique of secure multi-party computation (MPC) serves as the primary underlying tool in designing our system. At the heart of our system lies a new duplicity check protocol and an improved matching protocol. We also provide additional features such as allegation modification and deletion, which were absent in the state of the art. To demonstrate feasibility, we benchmark the proposed system with state-of-the-art MPC protocols and report the cost of processing an allegation. Different settings that affect system performance are analyzed, and the reported values showcase the practicality of our solution

Attaining GOD Beyond Honest Majority With Friends and Foes

In the classical notion of multiparty computation (MPC), an honest party learning private inputs of others, either as a part of protocol specification or due to a malicious party\u27s unspecified messages, is not considered a potential breach. Several works in the literature exploit this seemingly minor loophole to achieve the strongest security of guaranteed output delivery via a trusted third party, which nullifies the purpose of MPC. Alon et al. (CRYPTO 2020) presented the notion of Friends and Foes ($\mathtt{FaF}$) security, which accounts for such undesired leakage towards honest parties by modelling them as semi-honest (friends) who do not collude with malicious parties (foes). With real-world applications in mind, it\u27s more realistic to assume parties are semi-honest rather than completely honest, hence it is imperative to design efficient protocols conforming to the $\mathtt{FaF}$ security model. Our contributions are not only motivated by the practical viewpoint, but also consider the theoretical aspects of $\mathtt{FaF}$ security. We prove the necessity of semi-honest oblivious transfer for $\mathtt{FaF}$-secure protocols with optimal resiliency. On the practical side, we present QuadSquad, a ring-based 4PC protocol, which achieves fairness and GOD in the $\mathtt{FaF}$ model, with an optimal corruption of $1$ malicious and $1$ semi-honest party. QuadSquad is, to the best of our knowledge, the first practically efficient $\mathtt{FaF}$ secure protocol with optimal resiliency. Its performance is comparable to the state-of-the-art dishonest majority protocols while improving the security guarantee from abort to fairness and GOD. Further, QuadSquad elevates the security by tackling a stronger adversarial model over the state-of-the-art honest-majority protocols, while offering a comparable performance for the input-dependent computation. We corroborate these claims by benchmarking the performance of QuadSquad. We also consider the application of liquidity matching that deals with highly sensitive financial transaction data, where $\mathtt{FaF}$ security is apt. We design a range of $\mathtt{FaF}$ secure building blocks to securely realize liquidity matching as well as other popular applications such as privacy-preserving machine learning (PPML). Inclusion of these blocks makes QuadSquad a comprehensive framework

Find Thy Neighbourhood: Privacy-Preserving Local Clustering

Identifying a cluster around a seed node in a graph, termed local clustering, finds use in several applications, including fraud detection, targeted advertising, community detection, etc. However, performing local clustering is challenging when the graph is distributed among multiple data owners, which is further aggravated by the privacy concerns that arise in disclosing their view of the graph. This necessitates designing solutions for privacy-preserving local clustering and is addressed for the first time in the literature. We propose using the technique of secure multiparty computation (MPC) to achieve the same. Our local clustering algorithm is based on the heat kernel PageRank (HKPR) metric, which produces the best-known cluster quality. En route to our final solution, we have two important steps: (i) designing data-oblivious equivalent of the state-of-the-art algorithms for computing local clustering and HKPR values, and (ii) compiling the data-oblivious algorithms into its secure realisation via an MPC framework that supports operations over fixed-point arithmetic representation such as multiplication and division. Keeping efficiency in mind for large graphs, we choose the best-known honest-majority 3-party framework of SWIFT (Koti et al., USENIX\u2721) and enhance it with some of the necessary yet missing primitives, before using it for our purpose. We benchmark the performance of our secure protocols, and the reported run time showcases the practicality of the same. Further, we perform extensive experiments to evaluate the accuracy loss of our protocols. Compared to their cleartext counterparts, we observe that the results are comparable and thus showcase the practicality of the designed protocols

Vogue: Faster Computation of Private Heavy Hitters

Consider the problem of securely identifying τ -heavy hitters, where given a set of client inputs, the goal is to identify those inputs which are held by at least τ clients in a privacy-preserving manner. Towards this, we design a novel system Vogue, whose key highlight in comparison to prior works, is that it ensures complete privacy and does not leak any information other than the heavy hitters. In doing so, Vogue aims to achieve as efficient a solution as possible. To showcase these efficiency improvements, we benchmark our solution and observe that it requires around 14 minutes to compute the heavy hitters for τ = 900 on 256-bit inputs when considering 400K clients. This is in contrast to the state of the art solution that requires over an hour for the same. In addition to the static input setting described above, Vogue also accounts for streaming inputs and provides a protocol that outperforms the state-of-the-art therein. The efficiency improvements witnessed while computing heavy hitters in both, the static and streaming input settings, are attributed to our new secure stable compaction protocol, whose round complexity is independent of the size of the input array to be compacte

Ruffle: Rapid 3-party shuffle protocols

Secure shuffle is an important primitive that finds use in several applications such as secure electronic voting, oblivious RAMs, secure sorting, to name a few. For time-sensitive shuffle-based applications that demand a fast response time, it is essential to design a fast and efficient shuffle protocol. In this work, we design secure and fast shuffle protocols relying on the techniques of secure multiparty computation. We make several design choices that aid in achieving highly efficient protocols. Specifically, we consider malicious 3-party computation setting with an honest majority and design robust ring-based protocols. Our shuffle protocols provide a fast online (i.e., input-dependent) phase compared to the state-of-the-art for the considered setting. To showcase the efficiency improvements brought in by our shuffle protocols, we consider two distinct applications of anonymous broadcast and secure graph computation via the GraphSC paradigm. In both cases, multiple shuffle invocations are required. Hence, going beyond standalone shuffle invocation, we identify two distinct scenarios of multiple invocations and provide customised protocols for the same. Further, we showcase that our customized protocols not only provide a fast response time, but also provide improved overall run time for multiple shuffle invocations. With respect to the applications, we not only improve in terms of efficiency, but also work towards providing improved security guarantees, thereby outperforming the respective state-of-the-art works. We benchmark our shuffle protocols and the considered applications to analyze the efficiency improvements with respect to various parameters