Reputation-based partition scheme for IoT security
- URL: http://arxiv.org/abs/2508.03981v1
- Date: Wed, 06 Aug 2025 00:27:59 GMT
- Title: Reputation-based partition scheme for IoT security
- Abstract summary: Crowdsensing is an emerging data aggregation paradigm, which plays a pivotal role in data-driven applications.<n>There are some key issues in the development of crowdsensing such as platform security and privacy protection.<n>An effective reputation-based partition scheme (RSPC) is proposed in this article.
- Score: 21.32565499244062
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: With the popularity of smart terminals, such as the Internet of Things, crowdsensing is an emerging data aggregation paradigm, which plays a pivotal role in data-driven applications. There are some key issues in the development of crowdsensing such as platform security and privacy protection. As the crowdsensing is usually managed by a centralized platform, centralized management will bring various security vulnerabilities and scalability issues. To solve these issues, an effective reputation-based partition scheme (RSPC) is proposed in this article. The partition scheme calculates the optimal partition size by combining the node reputation value and divides the node into several disjoint partitions according to the node reputation value. By selecting the appropriate partition size, RSPC provides a mechanism to ensure that each partition is valid, as long as themaximum permissible threshold for the failed node is observed. At the same time, the RSPC reorganizes the network periodically to avoid partition attacks. In addition, for cross-partition transactions, this paper innovatively proposes a four-stage confirmation protocol to ensure the efficient and safe completion of cross-partition transactions. Finally, experiments show that RSPC improves scalability, low latency, and high throughput for crowdsensing.
Related papers
- Detecting and Mitigating Backdoor Attacks in OTA-FL Systems: A Two-Stage Robust Aggregation Scheme [27.75838619796163]
Over-the-air federated learning (OTA-FL) improves communication efficiency by exploiting the superposition property of wireless channels.<n>This creates a critical security vulnerability: the parameter server cannot access individual local updates.<n>We propose a two-stage robust aggregation framework for defending against backdoor attacks in OTA-FL.
arXiv Detail & Related papers (2026-05-19T01:57:31Z) - Joint Optimization of Model Partitioning and Resource Allocation for Anti-Jamming Collaborative Inference Systems [52.842088497389746]
This letter focuses on an anti-jamming collaborative inference system in the presence of a malicious jammer.<n>We first analyze the effects of jamming and DNN partitioning on inference accuracy via data regression.<n>We propose an efficient alternating optimization-based algorithm, which decomposes the problem into three subproblems.
arXiv Detail & Related papers (2026-03-03T03:52:52Z) - A Secure and Private Distributed Bayesian Federated Learning Design [56.92336577799572]
Distributed Federated Learning (DFL) enables decentralized model training across large-scale systems without a central parameter server.<n>DFL faces three critical challenges: privacy leakage from honest-but-curious neighbors, slow convergence due to the lack of central coordination, and vulnerability to Byzantine adversaries aiming to degrade model accuracy.<n>We propose a novel DFL framework that integrates Byzantine robustness, privacy preservation, and convergence acceleration.
arXiv Detail & Related papers (2026-02-23T16:12:02Z) - PiTPM: Partially Interactive Signatures for Multi-Device TPM Operations [0.4125187280299247]
This paper presents PiTPM, an Aggregator Framework built upon Schnorr's digital signature.<n>Our protocol eliminates the interactive requirement using a hybrid trust architecture.<n>Results show a possible paradigm shift in TPM-based cryptographic system design.
arXiv Detail & Related papers (2026-02-10T12:09:05Z) - BlocksecRT-DETR: Decentralized Privacy-Preserving and Token-Efficient Federated Transformer Learning for Secure Real-Time Object Detection in ITS [0.0]
Federated real-time object detection using transformers in Intelligent Transportation Systems (ITS) faces three major challenges.<n>missing-class non-IID data from geographically diverse traffic environments, latency constraints on edge hardware for high-capacity transformer models, and privacy and security risks from untrusted client updates and centralized aggregation.<n>We propose BlockSecRT-DETR, a decentralized, token-efficient, and privacy-preserving federated training solution using RT-DETR transformer.
arXiv Detail & Related papers (2026-01-19T03:29:55Z) - Perfectly-Private Analog Secure Aggregation in Federated Learning [51.61616734974475]
In federated learning, multiple parties train models locally and share their parameters with a central server, which aggregates them to update a global model.<n>In this paper, a novel secure parameter aggregation method is proposed that employs the torus rather than a finite field.
arXiv Detail & Related papers (2025-09-10T15:22:40Z) - PRISM: Distributed Inference for Foundation Models at Edge [73.54372283220444]
PRISM is a communication-efficient and compute-aware strategy for distributed Transformer inference on edge devices.<n>We evaluate PRISM on ViT, BERT, and GPT-2 across diverse datasets.
arXiv Detail & Related papers (2025-07-16T11:25:03Z) - FRAG: Toward Federated Vector Database Management for Collaborative and Secure Retrieval-Augmented Generation [1.3824176915623292]
This paper introduces textitFederated Retrieval-Augmented Generation (FRAG), a novel database management paradigm tailored for the growing needs of retrieval-augmented generation (RAG) systems.
FRAG enables mutually-distrusted parties to collaboratively perform Approximate $k$-Nearest Neighbor (ANN) searches on encrypted query vectors and encrypted data stored in distributed vector databases.
arXiv Detail & Related papers (2024-10-17T06:57:29Z) - Federated Instruction Tuning of LLMs with Domain Coverage Augmentation [87.49293964617128]
Federated Domain-specific Instruction Tuning (FedDIT) utilizes limited cross-client private data together with various strategies of instruction augmentation.<n>We propose FedDCA, which optimize domain coverage through greedy client center selection and retrieval-based augmentation.<n>For client-side computational efficiency and system scalability, FedDCA$*$, the variant of FedDCA, utilizes heterogeneous encoders with server-side feature alignment.
arXiv Detail & Related papers (2024-09-30T09:34:31Z) - BULKHEAD: Secure, Scalable, and Efficient Kernel Compartmentalization with PKS [16.239598954752594]
Kernel compartmentalization is a promising approach that follows the least-privilege principle.
We present BULKHEAD, a secure, scalable, and efficient kernel compartmentalization technique.
We implement a prototype system on Linux v6.1 to compartmentalize loadable kernel modules.
arXiv Detail & Related papers (2024-09-15T04:11:26Z) - Privacy-Preserving Distributed Learning for Residential Short-Term Load
Forecasting [11.185176107646956]
Power system load data can inadvertently reveal the daily routines of residential users, posing a risk to their property security.
We introduce a Markovian Switching-based distributed training framework, the convergence of which is substantiated through rigorous theoretical analysis.
Case studies employing real-world power system load data validate the efficacy of our proposed algorithm.
arXiv Detail & Related papers (2024-02-02T16:39:08Z) - ByzSecAgg: A Byzantine-Resistant Secure Aggregation Scheme for Federated Learning Based on Coded Computing and Vector Commitment [61.540831911168226]
ByzSecAgg is an efficient secure aggregation scheme for federated learning.<n>ByzSecAgg is resistant to Byzantine attacks and privacy leakages.
arXiv Detail & Related papers (2023-02-20T11:15:18Z) - ScionFL: Efficient and Robust Secure Quantized Aggregation [36.668162197302365]
We introduce ScionFL, the first secure aggregation framework for federated learning.
It operates efficiently on quantized inputs and simultaneously provides robustness against malicious clients.
We show that with no overhead for clients and moderate overhead for the server, we obtain comparable accuracy for standard FL benchmarks.
arXiv Detail & Related papers (2022-10-13T21:46:55Z) - Receptive Field-based Segmentation for Distributed CNN Inference
Acceleration in Collaborative Edge Computing [93.67044879636093]
We study inference acceleration using distributed convolutional neural networks (CNNs) in collaborative edge computing network.
We propose a novel collaborative edge computing using fused-layer parallelization to partition a CNN model into multiple blocks of convolutional layers.
arXiv Detail & Related papers (2022-07-22T18:38:11Z) - Is Vertical Logistic Regression Privacy-Preserving? A Comprehensive
Privacy Analysis and Beyond [57.10914865054868]
We consider vertical logistic regression (VLR) trained with mini-batch descent gradient.
We provide a comprehensive and rigorous privacy analysis of VLR in a class of open-source Federated Learning frameworks.
arXiv Detail & Related papers (2022-07-19T05:47:30Z) - Sharing classical secrets with continuous-variable entanglement:
Composable security and network coding advantage [0.913755431537592]
We show that multi-partite entangled resources achieve a genuine advantage over point-to-point protocols for quantum communication.
This is the first concrete compelling examples of multi-partite entangled resources achieving a genuine advantage over point-to-point protocols for quantum communication.
arXiv Detail & Related papers (2021-04-21T17:37:28Z) - Adaptive Subcarrier, Parameter, and Power Allocation for Partitioned
Edge Learning Over Broadband Channels [69.18343801164741]
partitioned edge learning (PARTEL) implements parameter-server training, a well known distributed learning method, in wireless network.
We consider the case of deep neural network (DNN) models which can be trained using PARTEL by introducing some auxiliary variables.
arXiv Detail & Related papers (2020-10-08T15:27:50Z)
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.