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2025
 Jimenez-Berenguel, Andrea;  Gil, César;  García-Rubio, Carlos;  Forné, Jordi;  Campo, Celeste
DNS Query Forgery: A Client-Side Defense Against Mobile App Traffic Profiling Journal Article 
In: IEEE Access, vol. 13, pp. 1-20, 2025, ISSN: 2169-3536.
Abstract | Links | BibTeX | Tags: compromise, Data Perturbation Techniques, Discovery, dns traffic, I-Shaper, Privacy-Enhancing Technologies, Query Forgery, Qursa, user privacy, User Profiling
@article{Andrea002,

title = {DNS Query Forgery: A Client-Side Defense Against Mobile App Traffic Profiling},

author = {Andrea Jimenez-Berenguel and César Gil and Carlos García-Rubio and Jordi Forné and Celeste Campo},

url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11250988

https://ieeexplore.ieee.org/document/11250988

},

doi = {https://doi.org/10.1109/ACCESS.2025.3633695},

issn = {2169-3536},

year  = {2025},

date = {2025-11-17},

urldate = {2025-11-17},

journal = {IEEE Access},

volume = {13},

pages = {1-20},

abstract = {Mobile applications generate DNS queries that expose user behavioral patterns to network observers, creating privacy vulnerabilities even when communications are encrypted. Network eavesdroppers and DNS resolvers can analyze domain name sequences to profile users based on their app usage patterns. This paper proposes a client-side defense mechanism based on DNS query forgery to obfuscate user DNS-based profiles. Our method applies a query forgery technique that consists of injecting false DNS queries into genuine traffic streams. We mathematically model user profiles as probability distributions over interest categories and analyze the optimal proportion of false queries needed to achieve desired privacy levels. We evaluate three query forgery strategies: Uniform, TrackMeNot-based, and Optimized, finding that the Optimized strategy using KL divergence is the most effective. To validate our approach, we develop a novel methodology for generating synthetic user traces, creating a dataset of 1,000 users by mapping real app traffic data onto individual user profiles. Our analysis reveals that 50% privacy improvement is achievable with less than 20% traffic overhead, while 100% privacy protection requires approximately 40-60% additional traffic. We further propose a modular system architecture for practical implementation on mobile devices. This work offers a client-side privacy solution that operates without third-party trust requirements, empowering users to defend against traffic analysis without compromising application functionality.},

keywords = {compromise, Data Perturbation Techniques, Discovery, dns traffic, I-Shaper, Privacy-Enhancing Technologies, Query Forgery, Qursa, user privacy, User Profiling},

pubstate = {published},

tppubtype = {article}

}

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Mobile applications generate DNS queries that expose user behavioral patterns to network observers, creating privacy vulnerabilities even when communications are encrypted. Network eavesdroppers and DNS resolvers can analyze domain name sequences to profile users based on their app usage patterns. This paper proposes a client-side defense mechanism based on DNS query forgery to obfuscate user DNS-based profiles. Our method applies a query forgery technique that consists of injecting false DNS queries into genuine traffic streams. We mathematically model user profiles as probability distributions over interest categories and analyze the optimal proportion of false queries needed to achieve desired privacy levels. We evaluate three query forgery strategies: Uniform, TrackMeNot-based, and Optimized, finding that the Optimized strategy using KL divergence is the most effective. To validate our approach, we develop a novel methodology for generating synthetic user traces, creating a dataset of 1,000 users by mapping real app traffic data onto individual user profiles. Our analysis reveals that 50% privacy improvement is achievable with less than 20% traffic overhead, while 100% privacy protection requires approximately 40-60% additional traffic. We further propose a modular system architecture for practical implementation on mobile devices. This work offers a client-side privacy solution that operates without third-party trust requirements, empowering users to defend against traffic analysis without compromising application functionality.
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https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11250988
https://ieeexplore.ieee.org/document/11250988
doi:https://doi.org/10.1109/ACCESS.2025.3633695
Close