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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 SPDZ2k), 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 numerous linear computation 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 x T2 and T2 x T3 respectively, SPDZ2k required O(T1 x T2 x T3) local multiplications for the tag computation. In contrast, CompactTag only requires O(T1 x T2 + T1 x T3 + T2 x T3) local multiplications, resulting in a substantial performance boost for various ML models. We empirically compared our protocol to the SPDZ2k protocol for various ML circuits, including ResNet Training-Inference, Transformer Training-Inference, and VGG16 Training-Inference. SPDZ2k dedicated around 30% of its online runtime for tag computation. CompactTag speeds up this tag computation bottleneck by up to 23x, resulting in up to 1.47x total online phase runtime speedups for various ML workloads.

Multi-party computing (MPC) has been gaining popularity as a secure computing model over the past few years. However, prior works have demonstrated that MPC protocols still pay substantial performance penalties compared to plaintext, particularly when applied to ML algorithms. The overhead is due to added computation and communication costs. Prior studies, as well as our own analysis, found that most MPC protocols today sequentially perform communication and computation. The participating parties must compute on their shares first and then perform data communication to allow the distribution of new secret shares before proceeding to the next computation step. In this work, we show that serialization is unnecessary, particularly in the context of ML computations (both in Convolutional neural networks and in Transformer-based models). We demonstrate that it is possible to carefully orchestrate the computation and communication steps to overlap. We propose MPC-Pipe, an efficient MPC system for both training and inference of ML workloads, which pipelines computations and communications in an MPC protocol during the online phase. MPC-Pipe proposes three pipeline schemes to optimize the online phase of ML in the semi-honest majority adversary setting. We implement MPC-Pipe by augmenting a modified version of CrypTen, which separates online and offline phases. We evaluate the end-to-end system performance benefits of the online phase of MPC using deep neural networks (VGG16, ResNet50) and Transformers using different network settings. We show that MPC-Pipe can improve the throughput and latency of ML workloads., Comment: To be appeared in ASPLOS'25

Privacy and security-related concerns are growing as machine learning reaches diverse application domains. The data holders want to train or infer with private data while exploiting accelerators, such as GPUs, that are hosted in the cloud. Cloud systems are vulnerable to attackers that compromise the privacy of data and integrity of computations. Tackling such a challenge requires unifying theoretical privacy algorithms with hardware security capabilities. This paper presents DarKnight, a framework for large DNN training while protecting input privacy and computation integrity. DarKnight relies on cooperative execution between trusted execution environments (TEE) and accelerators, where the TEE provides privacy and integrity verification, while accelerators perform the bulk of the linear algebraic computation to optimize the performance. In particular, DarKnight uses a customized data encoding strategy based on matrix masking to create input obfuscation within a TEE. The obfuscated data is then offloaded to GPUs for fast linear algebraic computation. DarKnight's data obfuscation strategy provides provable data privacy and computation integrity in the cloud servers. While prior works tackle inference privacy and cannot be utilized for training, DarKnight's encoding scheme is designed to support both training and inference., Comment: MICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture. arXiv admin note: text overlap with arXiv:2105.00334

In this work, we present novel protocols over rings for semi-honest secure three-party computation (3PC) and malicious four-party computation (4PC) with one corruption. While most existing works focus on improving total communication complexity, challenges such as network heterogeneity and computational complexity, which impact MPC performance in practice, remain underexplored. Our protocols address these issues by tolerating multiple arbitrarily weak network links between parties without any substantial decrease in performance. Additionally, they significantly reduce computational complexity by requiring up to half the number of basic instructions per gate compared to related work. These improvements lead to up to twice the throughput of state-of-the-art protocols in homogeneous network settings and up to eight times higher throughput in real-world heterogeneous settings. These advantages come at no additional cost: Our protocols maintain the best-known total communication complexity per multiplication, requiring 3 elements for 3PC and 5 elements for 4PC. We implemented our protocols alongside several state-of-the-art protocols (Replicated 3PC, ASTRA, Fantastic Four, Tetrad) in a novel open-source C++ framework optimized for high throughput. Five out of six implemented 3PC and 4PC protocols achieve more than one billion 32-bit multiplications or over 32 billion AND gates per second using our implementation in a 25 Gbit/s LAN environment. This represents the highest throughput achieved in 3PC and 4PC so far, outperforming existing frameworks like MP-SPDZ, ABY3, MPyC, and MOTION by two to three orders of magnitude., Comment: This is the public version of the paper to be published at the 25th Privacy Enhancing Technologies Symposium (PETS 2025)

Data confidentiality is becoming a significant concern, especially in the cloud computing era. Memory access patterns have been demonstrated to leak critical information such as security keys and a program's spatial and temporal information. This information leak poses an even more significant privacy challenge in machine learning models with embedding tables. Embedding tables are routinely used to learn categorical features from training data. Even knowing the locations of the embedding table entries accessed, not the data within the embedding table, will compromise categorical input data to the model. Embedding entries are privacy-sensitive since they disclose valuable properties about the user. Oblivious RAM (ORAM), and its enhanced variants such as PathORAM have emerged as viable solutions to hide leakage from memory access streams. In this work, we present LAORAM, an ORAM framework explicitly designed to protect user privacy during embedding table training. LAORAM exploits the unique property of training, the training samples used in the future are known beforehand. LAORAM preprocesses the training samples to identify the memory blocks which are accessed together in the near future. The system tries to assign these blocks to as few paths as possible within the PathORAM infrastructure. LAORAM does this operation by combining multiple blocks accessed together as superblocks. To further increase performance, LAORAM uses a fat-tree structure for PathORAM reducing the number of background evictions required, which improves the stash usage. We have evaluated LAORAM using both a recommendation model (DLRM) and a NLP model (XLM-R) embedding table configurations. LAORAM performs 5 times faster than PathORAM on a recommendation dataset (Kaggle) and 5.4x faster on a NLP dataset (XNLI), while guaranteeing the same security guarantees as the original PathORAM.

Federated learning has emerged as a popular paradigm for collaboratively training a model from data distributed among a set of clients. This learning setting presents, among others, two unique challenges: how to protect privacy of the clients' data during training, and how to ensure integrity of the trained model. We propose a two-pronged solution that aims to address both challenges under a single framework. First, we propose to create secure enclaves using a trusted execution environment (TEE) within the server. Each client can then encrypt their gradients and send them to verifiable enclaves. The gradients are decrypted within the enclave without the fear of privacy breaches. However, robustness check computations in a TEE are computationally prohibitive. Hence, in the second step, we perform a novel gradient encoding that enables TEEs to encode the gradients and then offloading Byzantine check computations to accelerators such as GPUs. Our proposed approach provides theoretical bounds on information leakage and offers a significant speed-up over the baseline in empirical evaluation.

Privacy and security-related concerns are growing as machine learning reaches diverse application domains. The data holders want to train with private data while exploiting accelerators, such as GPUs, that are hosted in the cloud. However, Cloud systems are vulnerable to attackers that compromise the privacy of data and integrity of computations. This work presents DarKnight, a framework for large DNN training while protecting input privacy and computation integrity. DarKnight relies on cooperative execution between trusted execution environments (TEE) and accelerators, where the TEE provides privacy and integrity verification, while accelerators perform the computation heavy linear algebraic operations.

Federated learning (FL) is a promising paradigm for training a global model over data distributed across multiple data owners without centralizing clients' raw data. However, sharing of local model updates can also reveal information of clients' local datasets. Trusted execution environments (TEEs) within the FL server have been recently deployed by companies like Meta for secure aggregation. However, secure aggregation can suffer from error-prone local updates sent by clients that become faulty during training due to underlying device malfunctions. Also, data heterogeneity across clients makes fault mitigation challenging, as even updates from normal clients are dissimilar. Thus, most of the prior fault tolerant methods, which treat any local update differing from the majority of other updates as faulty, perform poorly. We propose DiverseFL to make model aggregation secure as well as robust to faults. In DiverseFL, any client whose local model update diverges from its associated guiding update is tagged as being faulty. To implement our novel per-client criteria for fault mitigation, DiverseFL creates a TEE-based secure enclave within the FL server, which in addition to performing secure aggregation for carrying out the global model update step, securely receives a small representative sample of local data from each client only once before training, and computes guiding updates for each participating client during training. Thus, DiverseFL provides security against privacy leakage as well as robustness against faulty clients. In experiments, DiverseFL consistently achieves significant improvements in absolute test accuracy over prior fault mitigation benchmarks. DiverseFL also performs closely to OracleSGD, where server combines updates only from the normal clients. We also analyze the convergence rate of DiverseFL under non-IID data and standard convexity assumptions.

Protecting the privacy of input data is of growing importance as machine learning methods reach new application domains. In this paper, we provide a unified training and inference framework for large DNNs while protecting input privacy and computation integrity. Our approach called DarKnight uses a novel data blinding strategy using matrix masking to create input obfuscation within a trusted execution environment (TEE). Our rigorous mathematical proof demonstrates that our blinding process provides information-theoretic privacy guarantee by bounding information leakage. The obfuscated data can then be offloaded to any GPU for accelerating linear operations on blinded data. The results from linear operations on blinded data are decoded before performing non-linear operations within the TEE. This cooperative execution allows DarKnight to exploit the computational power of GPUs to perform linear operations while exploiting TEEs to protect input privacy. We implement DarKnight on an Intel SGX TEE augmented with a GPU to evaluate its performance.

This work presents Origami, which provides privacy-preserving inference for large deep neural network (DNN) models through a combination of enclave execution, cryptographic blinding, interspersed with accelerator-based computation. Origami partitions the ML model into multiple partitions. The first partition receives the encrypted user input within an SGX enclave. The enclave decrypts the input and then applies cryptographic blinding to the input data and the model parameters. Cryptographic blinding is a technique that adds noise to obfuscate data. Origami sends the obfuscated data for computation to an untrusted GPU/CPU. The blinding and de-blinding factors are kept private by the SGX enclave, thereby preventing any adversary from denoising the data, when the computation is offloaded to a GPU/CPU. The computed output is returned to the enclave, which decodes the computation on noisy data using the unblinding factors privately stored within SGX. This process may be repeated for each DNN layer, as has been done in prior work Slalom. However, the overhead of blinding and unblinding the data is a limiting factor to scalability. Origami relies on the empirical observation that the feature maps after the first several layers can not be used, even by a powerful conditional GAN adversary to reconstruct input. Hence, Origami dynamically switches to executing the rest of the DNN layers directly on an accelerator without needing any further cryptographic blinding intervention to preserve privacy. We empirically demonstrate that using Origami, a conditional GAN adversary, even with an unlimited inference budget, cannot reconstruct the input. We implement and demonstrate the performance gains of Origami using the VGG-16 and VGG-19 models. Compared to running the entire VGG-19 model within SGX, Origami inference improves the performance of private inference from 11x while using Slalom to 15.1x., Comment: 13 pages, Under submission