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White Mirror: Leaking Sensitive Information from Interactive Netflix Movies using Encrypted Traffic Analysis

Published: 19 August 2019 Publication History

Abstract

Privacy leaks from Netflix videos/movies are well researched. Current state-of-the-art works have been able to obtain coarse-grained information such as the genre and the title of videos by passive observation of encrypted traffic. However, leakage of fine-grained information from encrypted video traffic has not been studied so far. Such information can be used to build behavioral profiles of viewers.
Recently, Netflix released the first mainstream interactive movie called 'Black Mirror: Bandersnatch'. In this work, we use this movie as a case-study to develop techniques for revealing information from encrypted interactive video traffic. We show for the first time that information such as the choices made by viewers can be revealed based on the characteristics of encrypted control traffic exchanged with Netflix. To evaluate our proposed technique, we built the first interactive video traffic dataset of 100 viewers; which we will be releasing. Our technique was able to reveal the choices 96% of the time in the case of 'Black Mirror: Bandersnatch' and they were also equally or more successful for all other interactive movies released by Netflix so far.

References

[1]
2019. Interactive content on Netflix. Retrieved May 20, 2019 from https://help.netflix.com/en/node/62526
[2]
Gargi Mitra, Prasanna Karthik Vairam, Patanjali SLPSK, Nitin Chandrachoodan, Kamakoti V. 2019. Netflix interactive video traffic dataset. Github link: https://github.com/Gargi-Mitra/SIGCOMM2019-NetflixInteractive.git.
[3]
Feng Li, Jae Won Chung, and Mark Claypool. 2018. Silhouette: Identifying youtube video flows from encrypted traffic. In NOSSDAV. ACM, 19--24.
[4]
Andrew Reed and Michael Kranch. 2017. Identifying httpsprotected netflix videos in real-time. In CODASPY. ACM, 361--368.
[5]
Roei Schuster, Vitaly Shmatikov, and Eran Tromer. 2017. Beauty and the burst: Remote identification of encrypted video streams. In USENIX Security.

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    cover image ACM Conferences
    SIGCOMM Posters and Demos '19: Proceedings of the ACM SIGCOMM 2019 Conference Posters and Demos
    August 2019
    183 pages
    ISBN:9781450368865
    DOI:10.1145/3342280
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 19 August 2019

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    Author Tags

    1. Netflix interactive videos
    2. encrypted video traffic analysis
    3. privacy leak

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    • Short-paper
    • Research
    • Refereed limited

    Conference

    SIGCOMM '19
    Sponsor:
    SIGCOMM '19: ACM SIGCOMM 2019 Conference
    August 19 - 23, 2019
    Beijing, China

    Acceptance Rates

    SIGCOMM Posters and Demos '19 Paper Acceptance Rate 62 of 102 submissions, 61%;
    Overall Acceptance Rate 92 of 158 submissions, 58%

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