SecureRouter: Encrypted Routing for Efficient Secure Inference
- URL: http://arxiv.org/abs/2604.15499v1
- Date: Thu, 16 Apr 2026 20:18:12 GMT
- Title: SecureRouter: Encrypted Routing for Efficient Secure Inference
- Abstract summary: We present Secure inference, an end-to-end encrypted routing and inference framework.<n>Secure achieves a latency reduction by 1.95x with negligible accuracy loss.
- Score: 18.40709699874288
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Cryptographically secure neural network inference typically relies on secure computing techniques such as Secure Multi-Party Computation (MPC), enabling cloud servers to process client inputs without decrypting them. Although prior privacy-preserving inference systems co-design network optimizations with MPC, they remain slow and costly, limiting real-world deployment. A major bottleneck is their use of a single, fixed transformer model for all encrypted inputs, ignoring that different inputs require different model sizes to balance efficiency and accuracy. We present SecureRouter, an end-to-end encrypted routing and inference framework that accelerates secure transformer inference through input-adaptive model selection under encryption. SecureRouter establishes a unified encrypted pipeline that integrates a secure router with an MPC-optimized model pool, enabling coordinated routing, inference, and protocol execution while preserving full data and model confidentiality. The framework includes training-phase and inference-phase components: an MPC-cost-aware secure router that predicts per-model utility and cost from encrypted features, and an MPC-optimized model pool whose architectures and quantization schemes are co-trained to minimize MPC communication and computation overhead. Compared to prior work, SecureRouter achieves a latency reduction by 1.95x with negligible accuracy loss, offering a practical path toward scalable and efficient secure AI inference. Our open-source implementation is available at: https://github.com/UCF-ML-Research/SecureRouter
Related papers
- CR^2: Cost-Aware Risk-Controlled Routing for Wireless Device-Edge LLM Inference [52.849509991178884]
Large language models (LLMs) move from centralized clouds to mobile edge environments.<n> query-level routing between lightweight on-device models and stronger edge models provides a flexible mechanism to navigate this trade-off.<n>Existing routers are designed for centralized cloud settings and optimize token-level costs, failing to capture the dynamic latency and energy overheads in wireless edge deployments.
arXiv Detail & Related papers (2026-05-12T11:50:15Z) - Privacy-Preserving LLMs Routing [31.55544224550439]
We propose a privacy-preserving large language model routing framework (PPRoute)<n> PPRoute includes strategies to speed up encoder inference and nearest neighbor search under the Secure Multi-Party Computation (MPC) protocol.<n>It achieves approximately a 20$times$ speedup over nave MPC implementations.
arXiv Detail & Related papers (2026-04-17T06:02:27Z) - Design and Optimization of Cloud Native Homomorphic Encryption Workflows for Privacy-Preserving ML Inference [0.0]
Homomorphic Encryption (HE) has emerged as a compelling technique that enables cryptographic computation on encrypted data.<n>The integration of HE within large scale cloud native pipelines remains constrained by high computational overhead, orchestration complexity, and model compatibility issues.<n>This paper presents a systematic framework for the design and optimization of cloud native homomorphic encryption that support privacy ML inference.
arXiv Detail & Related papers (2025-10-28T15:13:32Z) - HHEML: Hybrid Homomorphic Encryption for Privacy-Preserving Machine Learning on Edge [4.623074730653607]
Privacy-preserving machine learning (PPML) is an emerging topic to handle secure machine learning inference over sensitive data in untrusted environments.<n>Fully homomorphic encryption (FHE) enables computation directly on encrypted data on the server side, but it introduces significant communication and computation overhead on the client side.<n>HHE addresses this limitation by combining symmetric encryption (SE) with FHE to reduce the computational cost on the client side.<n>Our work proposes a hardware-accelerated HHE architecture built around a lightweight symmetric cipher optimized for FHE compatibility.
arXiv Detail & Related papers (2025-10-23T05:51:55Z) - How Robust Are Router-LLMs? Analysis of the Fragility of LLM Routing Capabilities [62.474732677086855]
Large language model (LLM) routing has emerged as a crucial strategy for balancing computational costs with performance.<n>We propose the DSC benchmark: Diverse, Simple, and Categorized, an evaluation framework that categorizes router performance across a broad spectrum of query types.
arXiv Detail & Related papers (2025-03-20T19:52:30Z) - The Communication-Friendly Privacy-Preserving Machine Learning against Malicious Adversaries [14.232901861974819]
Privacy-preserving machine learning (PPML) is an innovative approach that allows for secure data analysis while safeguarding sensitive information.
We introduce efficient protocol for secure linear function evaluation.
We extend the protocol to handle linear and non-linear layers, ensuring compatibility with a wide range of machine-learning models.
arXiv Detail & Related papers (2024-11-14T08:55:14Z) - SLIP: Securing LLMs IP Using Weights Decomposition [3.097016309594195]
High cost of cloud-based deployment has spurred interest in running models on edge devices.<n>We introduce SLIP, a hybrid inference algorithm designed to protect edge-deployed models from theft.
arXiv Detail & Related papers (2024-07-15T16:37:55Z) - Transforming Image Super-Resolution: A ConvFormer-based Efficient Approach [58.57026686186709]
We introduce the Convolutional Transformer layer (ConvFormer) and propose a ConvFormer-based Super-Resolution network (CFSR)
CFSR inherits the advantages of both convolution-based and transformer-based approaches.
Experiments demonstrate that CFSR strikes an optimal balance between computational cost and performance.
arXiv Detail & Related papers (2024-01-11T03:08:00Z) - SOCI^+: An Enhanced Toolkit for Secure OutsourcedComputation on Integers [50.608828039206365]
We propose SOCI+ which significantly improves the performance of SOCI.
SOCI+ employs a novel (2, 2)-threshold Paillier cryptosystem with fast encryption and decryption as its cryptographic primitive.
Compared with SOCI, our experimental evaluation shows that SOCI+ is up to 5.4 times more efficient in computation and 40% less in communication overhead.
arXiv Detail & Related papers (2023-09-27T05:19:32Z) - An Adaptive Device-Edge Co-Inference Framework Based on Soft
Actor-Critic [72.35307086274912]
High-dimension parameter model and large-scale mathematical calculation restrict execution efficiency, especially for Internet of Things (IoT) devices.
We propose a new Deep Reinforcement Learning (DRL)-Soft Actor Critic for discrete (SAC-d), which generates the emphexit point, emphexit point, and emphcompressing bits by soft policy iterations.
Based on the latency and accuracy aware reward design, such an computation can well adapt to the complex environment like dynamic wireless channel and arbitrary processing, and is capable of supporting the 5G URL
arXiv Detail & Related papers (2022-01-09T09:31:50Z) - Harnessing Wireless Channels for Scalable and Privacy-Preserving
Federated Learning [56.94644428312295]
Wireless connectivity is instrumental in enabling federated learning (FL)
Channel randomnessperturbs each worker inversions model update while multiple workers updates incur significant interference on bandwidth.
In A-FADMM, all workers upload their model updates to the parameter server using a single channel via analog transmissions.
This not only saves communication bandwidth, but also hides each worker's exact model update trajectory from any eavesdropper.
arXiv Detail & Related papers (2020-07-03T16:31:15Z) - CryptoSPN: Privacy-preserving Sum-Product Network Inference [84.88362774693914]
We present a framework for privacy-preserving inference of sum-product networks (SPNs)
CryptoSPN achieves highly efficient and accurate inference in the order of seconds for medium-sized SPNs.
arXiv Detail & Related papers (2020-02-03T14:49:18Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.