Noisy Networks, Nosy Neighbors: Inferring Privacy Invasive Information from Encrypted Wireless Traffic

• URL: http://arxiv.org/abs/2510.13822v1
• Date: Sat, 27 Sep 2025 15:36:11 GMT
• Title: Noisy Networks, Nosy Neighbors: Inferring Privacy Invasive Information from Encrypted Wireless Traffic
• Abstract summary: This thesis explores the extent to which passive observation of wireless traffic in a smart home environment can be used to infer privacy-invasive information about its inhabitants.<n>We identify devices, infer their activity states and approximate their location using RSSI-based trilateration.<n>Our results show that privacy risks in smart homes extend beyond traditional data breaches.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This thesis explores the extent to which passive observation of wireless traffic in a smart home environment can be used to infer privacy-invasive information about its inhabitants. Using a setup that mimics the capabilities of a nosy neighbor in an adjacent flat, we analyze raw 802.11 packets and Bluetooth Low Energy advertisemets. From this data, we identify devices, infer their activity states and approximate their location using RSSI-based trilateration. Despite the encrypted nature of the data, we demonstrate that it is possible to detect active periods of multimedia devices, infer common activities such as sleeping, working and consuming media, and even approximate the layout of the neighbor's apartment. Our results show that privacy risks in smart homes extend beyond traditional data breaches: a nosy neighbor behind the wall can gain privacy-invasive insights into the lives of their neighbors purely from encrypted network traffic.

Related papers

• Noisy Networks, Nosy Neighbors: Simple Privacy Attacks Against Residential Wireless Traffic [1.1666234644810893]
  This paper investigates the extent to which a,casual, attacker'' with straightforward IT skills and no specialized cybersecurity or ML tooling can reproduce privacy attacks.<n>We demonstrate that this casual attacker can manually identify devices, recognize user states, track smartphone movements through walls via RSSI triangulation, and successfully extract detailed daily routines.<n>Our findings show that smart-home privacy leakage is a threat even from low-resourced, straightforward adversaries.
  arXiv  Detail & Related papers  (2026-05-04T13:01:14Z)
• WavInWav: Time-domain Speech Hiding via Invertible Neural Network [78.85443308774484]
  Previous audio hiding methods often result in unsatisfactory quality when recovering secret audio.<n>We use a flow-based invertible neural network to establish a direct link between stego audio, cover audio, and secret audio.<n>We also add an encryption technique to protect the hidden data from unauthorized access.
  arXiv  Detail & Related papers  (2025-10-03T11:36:16Z)
• Privacy-Preserving Secure Neighbor Discovery for Wireless Networks [0.0]
  Traditional Neighbor Discovery (ND) and Secure Neighbor Discovery (SND) are key elements for network functionality.<n>We present a novel Privacy-Preserving Secure Neighbor Discovery (PP-SND) protocol, enabling devices to perform SND without revealing their actual identities and locations.
  arXiv  Detail & Related papers  (2025-03-28T08:27:47Z)
• 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)
• 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)
• SoK: Secure Human-centered Wireless Sensing [3.9044773293857964]
  Human-centered wireless sensing (HCWS) aims to understand the fine-grained environment and activities of a human using the diverse wireless signals around him/her. The literature lacks a systematic understanding of the privacy vulnerabilities of wireless sensing and the defenses against them, resulting in the privacy-compromising HCWS design. First, we propose a signal processing pipeline to identify private information leakage and further understand the benefits and tradeoffs of wireless sensing-based inference attacks and defenses.
  arXiv  Detail & Related papers  (2022-11-22T08:36:56Z)
• Cross-Network Social User Embedding with Hybrid Differential Privacy Guarantees [81.6471440778355]
  We propose a Cross-network Social User Embedding framework, namely DP-CroSUE, to learn the comprehensive representations of users in a privacy-preserving way. In particular, for each heterogeneous social network, we first introduce a hybrid differential privacy notion to capture the variation of privacy expectations for heterogeneous data types. To further enhance user embeddings, a novel cross-network GCN embedding model is designed to transfer knowledge across networks through those aligned users.
  arXiv  Detail & Related papers  (2022-09-04T06:22:37Z)
• WiFi-based Spatiotemporal Human Action Perception [53.41825941088989]
  An end-to-end WiFi signal neural network (SNN) is proposed to enable WiFi-only sensing in both line-of-sight and non-line-of-sight scenarios. Especially, the 3D convolution module is able to explore thetemporal continuity of WiFi signals, and the feature self-attention module can explicitly maintain dominant features.
  arXiv  Detail & Related papers  (2022-06-20T16:03:45Z)
• A Wireless-Vision Dataset for Privacy Preserving Human Activity Recognition [53.41825941088989]
  A new WiFi-based and video-based neural network (WiNN) is proposed to improve the robustness of activity recognition. Our results show that WiVi data set satisfies the primary demand and all three branches in the proposed pipeline keep more than $80%$ of activity recognition accuracy.
  arXiv  Detail & Related papers  (2022-05-24T10:49:11Z)
• Privacy-Utility Trades in Crowdsourced Signal Map Obfuscation [20.58763760239068]
  Crowdsource celluar signal strength measurements can be used to generate signal maps to improve network performance. We consider obfuscating such data before the data leaves the mobile device. Our evaluation results, based on multiple, diverse, real-world signal map datasets, demonstrate the feasibility of concurrently achieving adequate privacy and utility.
  arXiv  Detail & Related papers  (2022-01-13T03:46:22Z)
• Detection, Tracking, and Counting Meets Drones in Crowds: A Benchmark [97.07865343576361]
  We construct a benchmark with a new drone-captured largescale dataset, named as DroneCrowd. We annotate 20,800 people trajectories with 4.8 million heads and several video-level attributes. We design the Space-Time Neighbor-Aware Network (STNNet) as a strong baseline to solve object detection, tracking and counting jointly in dense crowds.
  arXiv  Detail & Related papers  (2021-05-06T04:46:14Z)
• A GAN-based Approach for Mitigating Inference Attacks in Smart Home Environment [3.785123406103385]
  In this study, we explore the problem of adversaries spying on smart home users to infer sensitive information with the aid of machine learning techniques. We propose a Generative Adrial Network (GAN) based approach for privacy preservation in smart homes.
  arXiv  Detail & Related papers  (2020-11-13T02:14:32Z)

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.