Abstract In many unbalanced private set intersection (uPSI) applications of the client–server setting, the server needs to perform uPSI with multiple clients. Cong et al. (ACM CCS’21) proposed a state-of-the-art (SOTA) uPSI protocol based on fully homomorphic encryption (FHE), achieving malicious security by employing an oblivious pseudorandom function (OPRF) in the pre-processing phase. However, re-executing existing uPSI protocols with each client imposes significant computational overhead for the server. In this paper, we present JAGUAR, a maliciously secure and efficient uPSI protocol designed for this setting. JAGUAR reduces online computation through a Divide-and-Combine optimization, requiring only $${\mathcal {O}}(\sqrt{|X|})$$ O ( | X | ) homomorphic multiplications. Furthermore, it employs a novel fixed VOLE-based OPRF that enables reusable and lightweight pre-processing across multiple clients. Experimental results demonstrate that JAGUAR achieves up to $$2.7\times$$ 2.7 × improvement in online runtime compared to the SOTA protocol in LAN. In multi-client scenarios, JAGUAR further outperforms existing protocols by a wide margin in terms of scalability and overall performance.

Private set intersection (PSI) allows two participants to compute the intersection of their private sets without revealing any additional information beyond the intersection itself. It is known that oblivious linear evaluation (OLE) can be used to construct the online efficient PSI protocol. However, oblivious transfer (OT) and fully homomorphic encryption (FHE)-based offline OLE generation are expensive, and the online computational complexity is super-linear and still a heavy burden for large-scale sets. In this paper, we propose VCR, an efficient PSI protocol from vector OLE (VOLE) with the offline-online paradigm. Concretely, we first propose the batched short VOLE protocol to reduce offline overhead for generating VOLE tuples. Then, we design a batched private membership test protocol from pre-computed VOLE to accelerate the online computation. Experiments demonstrate that VCR outperforms prior art. Compared to state-of-the-art work, we reduce the total communication costs (resp. running time) by 341× and 9.1× (resp. 6.5× and 2.5×) on average for OT and FHE-based protocols.

Existing federated learning (FL) systems are highly vulnerable in terms of security and privacy due to their distributed architecture, facing poisoning attacks and inference attacks from adversaries. Some prior works have combined poisoning defenses with cryptographic tools: Secure Multi-Party Computation, Zero-Knowledge Proof, and Homomorphic Encryption to propose robust secure aggregation methods that provide security and privacy preservation for FL. Recently, Qin et al. (KDD’23) demonstrate that personalized federated learning (pFL) can effectively resist backdoor injection in poisoning attacks. In this paper, we analyze that as the number of malicious attackers increases, pFL remains vulnerable to backdoor attacks. Moreover, we reveal that current robust secure aggregation methods fail to offer efficient and robust backdoor defense for pFL. Therefore, we propose FLIGHT, a robust secure aggregation method for pFL. It implements a lightweight backdoor detection through a two-stage personalized defense mechanism and ensures privacy preservation using communication-efficient two-party secure computation (2PC) protocols. Extensive experiments on diverse datasets and neural networks validate that FLIGHT decreases run-time up to 64× compared by prior work RoFL (S&P’23), and 42× compared to FLAME (USENIX Security’22).

Secure neural network inference provides a promising solution to preserve the privacy of Deep Learning as a Service (DLaaS), but its substantial communication and computation overhead remain challenging. Recent works such as GForce [1] and Piranha [2] have introduced GPU-friendly secure inference protocols with improved computation efficiency, yet these approaches are either limited to supporting specialized-trained networks or expensive in communication. As a consequence, there remain potential improvements in functionalities and communication efficiency. To address the above challenges, we introduce Roger, a two-party secure inference framework with semi-honest security, designed to support general neural network inference with a reduced number of round complexity. Drawing inspiration from ABY2.0 [3], we propose the Partial-Fix technology, which fixes the share of one participant during the offline phase to improve its computation efficiency. Then, an online communication-free protocol for secure linear layer computation and a constant-round secure comparison protocol are proposed upon Partial-Fix. Implemented on top of Piranha, the experiments demonstrate that for the CIFAR10 dataset, a single inference on VGG16 requires only 0.40 seconds. In comparison to GForce (resp. Piranha), Roger at least achieves 1.20× (resp. 1.94×) improvement in LAN setting in terms of throughput.

Secure neural network inference enables server (model provider) and client to perform neural network inference without leaking their private inputs. Existing SOTA three-party computation (3PC) inference works emerge challenges on two fronts: i) GPU-accelerated CryptGPU (S&P'21) and P-FALCON (USENIX Security'22) face challenges related to high communication overhead. ii) communication-efficient Meteor(www'23) raises more computation burden and GPU memory usage. These challenges result in lower efficiency when handling large-scale batch inference requests on resource-constrained devices. In this work, we propose Comet,a communication-efficient batch secure three-party inference framework with client-aiding, which achieves semi-honest security in honest majority without collusion between the client and the servers. First, we propose client-aided sharing semantics, which leverages client-generated random values to enhance online communication efficiency. We also design efficient 3PC protocols for neural network operators based on GPU, improving the computational efficiency of both linear and nonlinear layers. Furthermore, we address the tradeoff between communication cost and GPU memory utilization, surpassing SOTA by 1.3-1.9× in communication, 1.5-3.8× in runtime on large-scale batch inference tasks.

Secure neural network inference has been a promising solution to private Deep-Learning-as-a-Service, which enables the service provider and user to execute neural network inference without revealing their private inputs. However, the expensive overhead of current schemes is still an obstacle when applied in real applications. In this work, we present Meteor, an online communication-efficient and fast secure 3-party computation neural network inference system aginst semi-honest adversary in honest-majority. The main contributions of Meteor are two-fold: i) We propose a new and improved 3-party secret sharing scheme stemming from the linearity of replicated secret sharing, and design efficient protocols for the basic cryptographic primitives, including linear operations, multiplication, most significant bit extraction, and multiplexer. ii) Furthermore, we build efficient and secure blocks for the widely used neural network operators such as Matrix Multiplication, ReLU, and Maxpool, along with exploiting several specific optimizations for better efficiency. Our total communication with the setup phase is a little larger than SecureNN (PoPETs’19) and Falcon (PoPETs’21), two state-of-the-art solutions, but the gap is not significant when the online phase must be optimized as a priority. Using Meteor, we perform extensive evaluations on various neural networks. Compared to SecureNN and Falcon, we reduce the online communication costs by up to 25.6 × and 1.5 ×, and improve the running-time by at most 9.8 × (resp. 8.1 ×) and 1.5 × (resp. 2.1 ×) in LAN (resp. WAN) for the online inference.