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Cited by 192Internet Traffic Analysis and Secure E-votingPrivacy-Preserving Technologies in DataPrivacy, Security, and Data Protection

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Keeping the Smart Home Private with Smart(er) IoT Traffic Shaping

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Apthorpe, Noah, Huang, Danny Yuxing, Reisman, Dillon, Narayanan, Arvind, Feamster, Nick·2019·Proceedings on Privacy Enhancing Technologies
DOI 10.2478/popets-2019-0040
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Abstract

The proliferation of smart home Internet of things (IoT) devices presents unprecedented challenges for preserving privacy within the home. In this paper, we demonstrate that a passive network observer (e.g., an Internet service provider) can infer private in-home activities by analyzing Internet traffic from commercially available smart home devices even when the devices use end-to-end transport-layer encryption. We evaluate common approaches for defending against these types of traffic analysis attacks, including firewalls, virtual private networks, and independent link padding, and find that none sufficiently conceal user activities with reasonable data overhead. We develop a new defense, “stochastic traffic padding” (STP), that makes it difficult for a passive network adversary to reliably distinguish genuine user activities from generated traffic patterns designed to look like user interactions. Our analysis provides a theoretical bound on an adversary’s ability to accurately detect genuine user activities as a function of the amount of additional cover traffic generated by the defense technique.

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  • Sensor data can lead to unexpected inferences about individuals and their behavior.

    “Several studies [9, 12, 40] have recently highlighted how sensor data can lead to unexpected inferences about individuals and their behavior.”

    From IoT Expunge: Implementing Verifiable Retention of IoT Data (2020) · cites this paper as [9]

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Cited by

Sorted by citations

  • IoT Expunge: Implementing Verifiable Retention of IoT Data

    Panwar, N., Sharma, S., Gupta, P., Ghosh, D., Mehrotra, S., & Venkatasubramanian, N. (2020) · Proceedings of the Tenth ACM Conference on Data and Application Security and Privacy · doi:10.1145/3374664.3375737 · Cited by 6

    “Several studies [9, 12, 40] have recently highlighted how sensor data can lead to unexpected inferences about individuals and their behavior.”
    Background

Metadata

Authors
Apthorpe, N., Huang, D. Y., Reisman, D., Narayanan, A., & Feamster, N.
Affiliations
Princeton University (US)
ORCID
Venue
Proceedings on Privacy Enhancing Technologies
Publisher
Privacy Enhancing Technologies Symposium Advisory Board
Published
2019-07-01
Volume
2019
Issue
3
Pages
128-148
ISSN
2299-0984 (ISSN-L)
DOI
10.2478/popets-2019-0040
OpenAlex
W2964344666
Cited by
192
References
0
Citations by year
2026: 10 · 2025: 17 · 2024: 19 · 2023: 35 · 2022: 34 · 2021: 27 · 2020: 44 · 2019: 6
FWCI
12.81
Type
journal article
Language
en
Open access
Licence
Locations
  • Proceedings on Privacy Enhancing Technologies (publishedVersion, cc-by-nc-nd)
  • arXiv (Cornell University) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
  • Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
  • Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
Keywords
smart home privacy, Internet of Things, traffic analysis attacks, end-to-end encryption, firewall, virtual private networks, cover traffic

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