Seagull: Privacy preserving network verification system

• URL: http://arxiv.org/abs/2402.08956v2
• Date: Sat, 08 Nov 2025 01:42:33 GMT
• Title: Seagull: Privacy preserving network verification system
• Authors: Jaber Daneshamooz, Melody Yu, Sucheer Maddury,
• Abstract summary: Border Gateway Protocol (BGP) serves as the core mechanism managing routing between autonomous systems.<n>Verifying the correctness and convergence of BGP configurations is essential for maintaining a stable and secure Internet.<n>This paper introduces a privacy-preserving verification framework that leverages multiparty computation.
• Score: 0.07646713951724012
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Internet relies on routing protocols to direct traffic efficiently across interconnected networks, with the Border Gateway Protocol (BGP) serving as the core mechanism managing routing between autonomous systems. However, BGP configurations are largely manual, making them susceptible to human errors that can lead to outages or security vulnerabilities. Verifying the correctness and convergence of BGP configurations is therefore essential for maintaining a stable and secure Internet. Yet, this verification process faces two key challenges: preserving the privacy of proprietary routing information and ensuring scalability across large, distributed networks. This paper introduces a privacy-preserving verification framework that leverages multiparty computation (MPC) to validate BGP configurations without exposing sensitive routing data. Our approach overcomes both privacy and scalability challenges by ensuring that no information beyond the verification outcome is revealed. Through formal analysis, we show that the proposed method achieves strong privacy guarantees and practical scalability, providing a secure and efficient foundation for verifying BGP-based routing in the Internet backbone.

Related papers

• Reliable and Private Anonymous Routing for Satellite Constellations [1.9499120576896225]
  This work proposes an enhanced anonymity architecture, evolving the Loopix mix-network, to provide robust security and reliability in volatile topologies.<n>We introduce three primary contributions: A multi-path transport protocol utilizing $(n, k)$ erasure codes, which is demonstrated to counteract the high link volatility and intermittent connectivity that renders standard mix-networks unreliable.<n>We validate this architecture via high-fidelity, packet-level simulations of a LEO constellation.
  arXiv  Detail & Related papers  (2026-02-12T09:43:55Z)
• Subgraph Federated Learning via Spectral Methods [52.40322201034717]
  FedLap is a novel framework that captures inter-node dependencies while ensuring privacy and scalability.<n>We provide a formal analysis of the privacy of FedLap, demonstrating that it preserves privacy.
  arXiv  Detail & Related papers  (2025-10-29T16:22:32Z)
• A Provably Secure Network Protocol for Private Communication with Analysis and Tracing Resistance [24.74468505942983]
  This paper proposes a novel decentralized anonymous routing protocol with resistance to tracing and traffic analysis.<n>It rigorously proves indistinguishable identity privacy for users even in highly adversarial environments.<n>The proposed protocol offers a provably secure solution for privacy-preserving communication in digital environments.
  arXiv  Detail & Related papers  (2025-08-03T10:50:04Z)
• Privacy-Preserving Anonymization of System and Network Event Logs Using Salt-Based Hashing and Temporal Noise [5.85293491327449]
  Event logs contain Personally Identifiable Information (PII)<n>Overly aggressive anonymization can destroy contextual integrity, while weak techniques risk re-identification through linkage or inference attacks.<n>This paper introduces novel field-specific anonymization methods that address this trade-off.
  arXiv  Detail & Related papers  (2025-07-29T15:16:42Z)
• Convergent Privacy Framework with Contractive GNN Layers for Multi-hop Aggregations [9.399260063250635]
  Differential privacy (DP) has been integrated into graph neural networks (GNNs) to protect sensitive structural information.<n>We propose a simple yet effective Contractive Graph Layer (CGL) that ensures the contractiveness required for theoretical guarantees.<n>Our framework, CARIBOU, supports both training and inference, equipped with a contractive aggregation module, a privacy allocation module, and a privacy auditing module.
  arXiv  Detail & Related papers  (2025-06-28T02:17:53Z)
• BEAR: BGP Event Analysis and Reporting [10.153790653358625]
  Border Gateway Protocol (BGP) anomalies can divert traffic through unauthorized or inefficient paths, jeopardizing network reliability and security.<n>BGP Event Analysis and Reporting framework generates comprehensive reports explaining detected BGP anomaly events.<n> BEAR achieves 100% accuracy, outperforming Chain-of-Thought and in-context learning baselines.
  arXiv  Detail & Related papers  (2025-06-04T23:34:36Z)
• Blockchain Powered Edge Intelligence for U-Healthcare in Privacy Critical and Time Sensitive Environment [0.559239450391449]
  We propose an autonomous computing model for privacy-critical and time-sensitive health applications.<n>The system supports continuous monitoring, real-time alert notifications, disease detection, and robust data processing and aggregation.<n>A secure access scheme is defined to manage both off-chain and on-chain data sharing and storage.
  arXiv  Detail & Related papers  (2025-05-31T06:58:52Z)
• Enhancing Privacy in Semantic Communication over Wiretap Channels leveraging Differential Privacy [51.028047763426265]
  Semantic communication (SemCom) improves transmission efficiency by focusing on task-relevant information. transmitting semantic-rich data over insecure channels introduces privacy risks. This paper proposes a novel SemCom framework that integrates differential privacy mechanisms to protect sensitive semantic features.
  arXiv  Detail & Related papers  (2025-04-23T08:42:44Z)
• SecureNT: A Practical Framework for Efficient Topology Protection and Monitoring [0.0]
  Network tomography plays a crucial role in network monitoring and management. Topology information can be inferred through end-to-end measurements using various inference algorithms. Existing protection methods attempt to secure topology information by manipulating end-to-end delay measurements. This paper presents a novel privacy-preserving framework that addresses these limitations.
  arXiv  Detail & Related papers  (2024-12-11T08:07:40Z)
• Collaborative Inference over Wireless Channels with Feature Differential Privacy [57.68286389879283]
  Collaborative inference among multiple wireless edge devices has the potential to significantly enhance Artificial Intelligence (AI) applications. transmitting extracted features poses a significant privacy risk, as sensitive personal data can be exposed during the process. We propose a novel privacy-preserving collaborative inference mechanism, wherein each edge device in the network secures the privacy of extracted features before transmitting them to a central server for inference.
  arXiv  Detail & Related papers  (2024-10-25T18:11:02Z)
• Decentralized Federated Anomaly Detection in Smart Grids: A P2P Gossip Approach [0.44328715570014865]
  This paper introduces a novel decentralized federated anomaly detection scheme based on two main gossip protocols namely Random Walk and Epidemic. Our approach yields a notable 35% improvement in training time compared to conventional Federated Learning.
  arXiv  Detail & Related papers  (2024-07-20T10:45:06Z)
• A Privacy-Preserving Graph Encryption Scheme Based on Oblivious RAM [0.0]
  We propose a novel graph encryption scheme designed to mitigate access pattern and query pattern leakage. Our solution establishes two key security objectives: (1) ensuring that adversaries, when presented with an encrypted graph, remain oblivious to any information regarding the underlying graph, and (2) achieving query indistinguishability by concealing access patterns.
  arXiv  Detail & Related papers  (2024-05-29T16:47:38Z)
• KiNETGAN: Enabling Distributed Network Intrusion Detection through Knowledge-Infused Synthetic Data Generation [0.0]
  We propose a knowledge-infused Generative Adversarial Network for generating synthetic network activity data (KiNETGAN) Our approach enhances the resilience of distributed intrusion detection while addressing privacy concerns.
  arXiv  Detail & Related papers  (2024-05-26T08:02:02Z)
• Private Online Community Detection for Censored Block Models [60.039026645807326]
  We study the private online change detection problem for dynamic communities, using a censored block model (CBM) We propose an algorithm capable of identifying changes in the community structure, while maintaining user privacy.
  arXiv  Detail & Related papers  (2024-05-09T12:35:57Z)
• Secure Routing for Mobile Ad hoc Networks [2.965855310793378]
  We present a route discovery protocol that mitigates the effects of malicious behavior in MANET networks. Our protocol guarantees that fabricated, compromised, or replayed route replies would either be rejected or never reach back the querying node. The scheme is robust in the presence of a number of non-colluding nodes.
  arXiv  Detail & Related papers  (2024-03-01T09:50:00Z)
• A Survey and Comparative Analysis of Security Properties of CAN Authentication Protocols [92.81385447582882]
  The Controller Area Network (CAN) bus leaves in-vehicle communications inherently non-secure. This paper reviews and compares the 15 most prominent authentication protocols for the CAN bus. We evaluate protocols based on essential operational criteria that contribute to ease of implementation.
  arXiv  Detail & Related papers  (2024-01-19T14:52:04Z)
• Adversarial Client Detection via Non-parametric Subspace Monitoring in the Internet of Federated Things [3.280202415151067]
  Internet of Federated Things (IoFT) represents a network of interconnected systems with federated learning as the backbone. We propose an effective non-parametric approach FedRR to address the adversarial attack problem. Our proposed method is capable of accurately detecting adversarial clients and controlling the false alarm rate under the scenario with no attack occurring.
  arXiv  Detail & Related papers  (2023-10-02T18:25:02Z)
• Robust and efficient verification of graph states in blind measurement-based quantum computation [52.70359447203418]
  Blind quantum computation (BQC) is a secure quantum computation method that protects the privacy of clients. It is crucial to verify whether the resource graph states are accurately prepared in the adversarial scenario. Here, we propose a robust and efficient protocol for verifying arbitrary graph states with any prime local dimension.
  arXiv  Detail & Related papers  (2023-05-18T06:24:45Z)
• Differentially Private Decentralized Optimization with Relay Communication [1.2695958417031445]
  We introduce a new measure: Privacy Leakage Frequency (PLF), which reveals the relationship between communication and privacy leakage of algorithms. A novel differentially private decentralized primal--dual algorithm named DP-RECAL is proposed to take advantage of operator splitting method and relay communication mechanism to experience less PLF.
  arXiv  Detail & Related papers  (2022-12-21T09:05:36Z)
• 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)
• Privacy-preserving Traffic Flow Prediction: A Federated Learning Approach [61.64006416975458]
  We propose a privacy-preserving machine learning technique named Federated Learning-based Gated Recurrent Unit neural network algorithm (FedGRU) for traffic flow prediction. FedGRU differs from current centralized learning methods and updates universal learning models through a secure parameter aggregation mechanism. It is shown that FedGRU's prediction accuracy is 90.96% higher than the advanced deep learning models.
  arXiv  Detail & Related papers  (2020-03-19T13:07:49Z)
• 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.