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doctoral thesis

Protecting privacy through metadata analysis

Siby, Sandra Deepthy  
2022

Although encryption hides the content of communications from third parties, metadata, i.e., the information attached to the content (such as the size or timing of communication) can be a rich source of details and context. In this dissertation, we demonstrate the power of metadata analysis. We illustrate ways in which we can use metadata analysis to protect privacy, by addressing two problems in the areas of network and web privacy.

In the first problem, we study recently standardized protocols such as encrypted DNS and QUIC. We show how metadata analysis can be used by adversaries to perform website-fingerprinting attacks against these protocols and to infer websites visited by a user, thereby compromising their privacy. We use the insights from our analysis to identify the requirements for developing effective countermeasures and to improve the resistance of these protocols to website fingerprinting. We find that hiding metadata is challenging.

In the second problem, we address the issue of online advertising and tracking services (ATS) that are constantly evolving to evade privacy protections established by browser vendors. We demonstrate how the very fact of metadata being hard to hide can be useful to defenders. Defenders can use metadata analysis to build ATS-detection systems that are more robust against adversarial evasion by capturing behavioral metadata of ATS. We use our findings to detect and counter the emergence of tracking via first-party cookies.

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Type
doctoral thesis
DOI
10.5075/epfl-thesis-8992
Author(s)
Siby, Sandra Deepthy  
Advisors
González Troncoso, Carmela  
Jury

Prof. Mathias Josef Payer (président) ; Prof. Carmela González Troncoso (directeur de thèse) ; Prof. Katerina Argyraki, Prof. Athina Markopoulou, Prof. Ben Stock (rapporteurs)

Date Issued

2022

Publisher

EPFL

Publisher place

Lausanne

Public defense year

2022-10-21

Thesis number

8992

Total of pages

200

Subjects

privacy

•

metadata

•

website fingerprinting

•

network protocols

•

online advertising and tracking

•

cookies

EPFL units
SPRING  
Faculty
IC  
School
IINFCOM  
Doctoral School
EDIC  
Available on Infoscience
October 17, 2022
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/191450
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