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pro vyhledávání: '"Goeckel, Dennis L."'
The Internet of Things (IoT) promises to improve user utility by tuning applications to user behavior, but revealing the characteristics of a user's behavior presents a significant privacy risk. Our previous work has established the challenging requi
Externí odkaz:
http://arxiv.org/abs/2007.06119
Autor:
Takbiri, Nazanin, Shejwalker, Virat, Houmansadr, Amir, Goeckel, Dennis L., Pishro-Nik, Hossein
The prevalence of mobile devices and Location-Based Services (LBS) necessitate the study of Location Privacy-Preserving Mechanisms (LPPM). However, LPPMs reduce the utility of LBS due to the noise they add to users' locations. Here, we consider the r
Externí odkaz:
http://arxiv.org/abs/1912.02209
Publikováno v:
The 53rd Annual Conference on Information Sciences and Systems 2019
Various modern and highly popular applications make use of user data traces in order to offer specific services, often for the purpose of improving the user's experience while using such applications. However, even when user data is privatized by emp
Externí odkaz:
http://arxiv.org/abs/1902.06404
The rapid growth of the Internet of Things (IoT) necessitates employing privacy-preserving techniques to protect users' sensitive information. Even when user traces are anonymized, statistical matching can be employed to infer sensitive information.
Externí odkaz:
http://arxiv.org/abs/1809.10289
Modern applications significantly enhance user experience by adapting to each user's individual condition and/or preferences. While this adaptation can greatly improve a user's experience or be essential for the application to work, the exposure of u
Externí odkaz:
http://arxiv.org/abs/1806.11108
Publikováno v:
ISIT 2018
Modern applications significantly enhance user experience by adapting to each user's individual condition and/or preferences. While this adaptation can greatly improve utility or be essential for the application to work (e.g., for ride-sharing applic
Externí odkaz:
http://arxiv.org/abs/1805.01296
Many popular applications use traces of user data to offer various services to their users. However, even if user data is anonymized and obfuscated, a user's privacy can be compromised through the use of statistical matching techniques that match a u
Externí odkaz:
http://arxiv.org/abs/1710.00197
Akademický článek
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Compressive sensing (CS) allows for acquisition of sparse signals at sampling rates significantly lower than the Nyquist rate required for bandlimited signals. Recovery guarantees for CS are generally derived based on the assumption that measurement
Externí odkaz:
http://arxiv.org/abs/1205.2118
Xue and Kumar have established that the number of neighbors required for connectivity of wireless networks with N uniformly distributed nodes must grow as log(N), and they also established that the actual number required lies between 0.074log(N) and
Externí odkaz:
http://arxiv.org/abs/cs/0509085