sNVMe-oF: Secure and Efficient Disaggregated Storage
- URL: http://arxiv.org/abs/2510.18756v1
- Date: Tue, 21 Oct 2025 16:01:36 GMT
- Title: sNVMe-oF: Secure and Efficient Disaggregated Storage
- Abstract summary: sNVMe-oF is a storage management system extending the disaggregated storage protocol.<n>We demonstrate how it can achieve as little as 2% performance degradation for synthetic patterns and AI training.
- Score: 16.71322861850533
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Disaggregated storage with NVMe-over-Fabrics (NVMe-oF) has emerged as the standard solution in modern data centers, achieving superior performance, resource utilization, and power efficiency. Simultaneously, confidential computing (CC) is becoming the de facto security paradigm, enforcing stronger isolation and protection for sensitive workloads. However, securing state-of-the-art storage with traditional CC methods struggles to scale and compromises performance or security. To address these issues, we introduce sNVMe-oF, a storage management system extending the NVMe-oF protocol and adhering to the CC threat model by providing confidentiality, integrity, and freshness guarantees. sNVMe-oF offers an appropriate control path and novel concepts such as counter-leasing. sNVMe-oF also optimizes data path performance by leveraging NVMe metadata, introducing a new disaggregated Hazel Merkle Tree (HMT), and avoiding redundant IPSec protections. We achieve this without modifying the NVMe-oF protocol. To prevent excessive resource usage while delivering line rate, sNVMe-oF also uses accelerators of CC-capable smart NICs. We prototype sNVMe-oF on an NVIDIA BlueField-3 and demonstrate how it can achieve as little as 2% performance degradation for synthetic patterns and AI training.
Related papers
- Byzantine-Robust and Differentially Private Federated Optimization under Weaker Assumptions [89.52532304099522]
Federated Learning (FL) enables clients to collaboratively train a shared model without centralizing their raw data, offering an inherent level of privacy.<n> gradients and model updates can still leak sensitive information, while malicious servers may mount adversarial attacks such as Byzantine manipulation.<n>These vulnerabilities highlight the need to address differential privacy (DP) and Byzantine robustness within a unified framework.<n>We propose Byz-Clip21-SGD2M, a new algorithm that integrates robust aggregation with double momentum and carefully designed clipping.
arXiv Detail & Related papers (2026-03-24T17:39:09Z) - Towards Secure and Efficient DNN Accelerators via Hardware-Software Co-Design [18.80243704372307]
This paper presents a secure and efficient memory protection framework for deep neural network (DNN) accelerators with minimal overhead.<n>First, we propose a bandwidth-aware cryptographic scheme that adapts encryption granularity based on memory traffic patterns.<n>Second, we observe that both the overlapping regions in the intra-layer's sliding window pattern and those resulting from inter-layer tiling strategy discrepancies introduce substantial redundant memory accesses.<n>Third, we introduce a multi-level authentication mechanism that effectively eliminates unnecessary off-chip memory accesses, enhancing performance and energy efficiency.
arXiv Detail & Related papers (2026-02-24T03:49:12Z) - MirageNet:A Secure, Efficient, and Scalable On-Device Model Protection in Heterogeneous TEE and GPU System [8.936421130707204]
Given high model training costs and user experience requirements, balancing model privacy and low runtime overhead is critical.<n>We propose ConvShatter, a novel obfuscation scheme that achieves low latency and high accuracy while preserving model confidentiality and integrity.
arXiv Detail & Related papers (2026-01-20T10:39:09Z) - When UAV Swarm Meets IRS: Collaborative Secure Communications in Low-altitude Wireless Networks [68.45202147860537]
Low-altitude wireless networks (LAWNs) provide enhanced coverage, reliability, and throughput for diverse applications.<n>These networks face significant security vulnerabilities from both known and potential unknown eavesdroppers.<n>We propose a novel secure communication framework for LAWNs where the selected UAVs within a swarm function as a virtual antenna array.
arXiv Detail & Related papers (2025-10-25T02:02:14Z) - Towards Reliable Service Provisioning for Dynamic UAV Clusters in Low-Altitude Economy Networks [48.73244147035607]
Unmanned Aerial Vehicle (UAV) cluster services are crucial for promoting the low-altitude economy by enabling scalable, flexible, and adaptive aerial networks.<n>We propose a Lightweight and Privacy-Preserving Cluster Authentication and Session Key Update (LP2-CA) scheme for dynamic UAV clusters in low-altitude economy networks.
arXiv Detail & Related papers (2025-09-07T15:54:11Z) - Designing a Layered Framework to Secure Data via Improved Multi Stage Lightweight Cryptography in IoT Cloud Systems [1.5803208833562954]
This paper presents a novel multi-layered hybrid security approach aimed at enhancing lightweight encryption for IoT-Cloud systems.<n>The proposed framework consists of three core layers: (1) the H.E.EZ Layer which integrates improved versions of Hyperledger Fabric, Enc-Block and a hybrid ECDSA-ZSS scheme to improve encryption speed, scalability and reduce computational cost; (2) the Credential Management Layer independently verifying data authenticity and authenticity; and (3) the Time and Auditing Layer designed to reduce traffic overhead and optimize performance across dynamic workloads.
arXiv Detail & Related papers (2025-09-01T18:53:20Z) - Selective KV-Cache Sharing to Mitigate Timing Side-Channels in LLM Inference [6.864810630905683]
Existing defenses, such as per-user isolation, eliminate leakage but degrade performance by up to 38.9% in time-to-first-token (TTFT)<n>We introduce SafeKV, a privacy-aware KV-cache management framework that selectively shares non-sensitive entries while confining sensitive content to private caches.<n>Our evaluation shows that SafeKV mitigates 94% - 97% of timing-based side-channel attacks.
arXiv Detail & Related papers (2025-08-11T19:55:44Z) - Safeguard Fine-Tuned LLMs Through Pre- and Post-Tuning Model Merging [47.33307521558814]
Fine-tuning large language models (LLMs) for downstream tasks often leads to catastrophic forgetting.<n>We show that simply merging the weights of pre- and post-fine-tuned models effectively mitigates safety degradation while enhancing performance.
arXiv Detail & Related papers (2024-12-27T08:03:22Z) - RISecure-PUF: Multipurpose PUF-Driven Security Extensions with Lookaside Buffer in RISC-V [12.294919757082608]
RISecure-PUF is a security extension utilizing existing Physical Unclonable Functions.
A one-way hash function is integrated to ensure provable security against modeling attacks.
RISecure-PUF improves at least $2.72times$ in batch scenarios with negligible hardware overhead.
arXiv Detail & Related papers (2024-11-21T11:26:23Z) - TinyML NLP Scheme for Semantic Wireless Sentiment Classification with Privacy Preservation [49.801175302937246]
This study provides insights into deploying privacy-preserving, energy-efficient NLP models on edge devices.<n>We introduce semantic split learning (SL) as an energy-efficient, privacy-preserving tiny machine learning (TinyML) framework.<n>Our results show that SL significantly reduces computational power and CO2 emissions while enhancing privacy, as evidenced by a fourfold increase in reconstruction error compared to FL and nearly eighteen times that of CL.
arXiv Detail & Related papers (2024-11-09T21:26:59Z) - Towards Building Secure UAV Navigation with FHE-aware Knowledge Distillation [0.0]
This paper proposes an innovative approach that leverages Knowledge Distillation to enhance the practicality of secure UAV navigation.
By integrating RL and FHE, our framework addresses vulnerabilities to adversarial attacks while enabling real-time processing of encrypted UAV camera feeds.
To mitigate FHE's latency, Knowledge Distillation is employed to compress the network, resulting in an impressive 18x speedup without compromising performance.
arXiv Detail & Related papers (2024-11-01T07:04:24Z) - Digital Twin-Assisted Data-Driven Optimization for Reliable Edge Caching in Wireless Networks [60.54852710216738]
We introduce a novel digital twin-assisted optimization framework, called D-REC, to ensure reliable caching in nextG wireless networks.
By incorporating reliability modules into a constrained decision process, D-REC can adaptively adjust actions, rewards, and states to comply with advantageous constraints.
arXiv Detail & Related papers (2024-06-29T02:40:28Z) - Contractive error feedback for gradient compression [60.05809370598166]
We propose a communication efficient method called contractive error feedback (ConEF)
As opposed to SGD with error-feedback (EFSGD) that inefficiently manages memory, ConEF obtains the sweet spot of convergence and memory usage.
We empirically validate ConEF on various learning tasks that include image classification, language modeling, and machine translation.
arXiv Detail & Related papers (2023-12-13T21:54:21Z) - A Safe Genetic Algorithm Approach for Energy Efficient Federated
Learning in Wireless Communication Networks [53.561797148529664]
Federated Learning (FL) has emerged as a decentralized technique, where contrary to traditional centralized approaches, devices perform a model training in a collaborative manner.
Despite the existing efforts made in FL, its environmental impact is still under investigation, since several critical challenges regarding its applicability to wireless networks have been identified.
The current work proposes a Genetic Algorithm (GA) approach, targeting the minimization of both the overall energy consumption of an FL process and any unnecessary resource utilization.
arXiv Detail & Related papers (2023-06-25T13:10:38Z)
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.