65 research outputs found
Asymptotic Loss in Privacy due to Dependency in Gaussian Traces
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. In our previous work, we have established the privacy
requirements for the case that the user traces are instantiations of discrete
random variables and the adversary knows only the structure of the dependency
graph, i.e., whether each pair of users is connected. In this paper, we
consider the case where data traces are instantiations of Gaussian random
variables and the adversary knows not only the structure of the graph but also
the pairwise correlation coefficients. We establish the requirements on
anonymization to thwart such statistical matching, which demonstrate the
significant degree to which knowledge of the pairwise correlation coefficients
further significantly aids the adversary in breaking user anonymity.Comment: IEEE Wireless Communications and Networking Conferenc
Performance Bounds for Grouped Incoherent Measurements in Compressive Sensing
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 projections are selected independently at random.
However, for many practical signal acquisition applications, including medical
imaging and remote sensing, this assumption is violated as the projections must
be taken in groups. In this paper, we consider such applications and derive
requirements on the number of measurements needed for successful recovery of
signals when groups of dependent projections are taken at random. We find a
penalty factor on the number of required measurements with respect to the
standard CS scheme that employs conventional independent measurement selection
and evaluate the accuracy of the predicted penalty through simulations.Comment: Revised for publication. 21 pages, 10 figure
An approximate analysis of heterogeneous and general cache networks
In this paper, we propose approximate models to assess the performance of a cache network with arbitrary topology where nodes run the Least Recently Used (LRU), First-In First-Out (FIFO), or Random (RND) replacement policies on arbitrary size caches. Our model takes advantage of the notions of cache characteristic time and Time-To-Live (TTL)-based cache to develop a unified framework for approximating metrics of interest of interconnected caches. Our approach is validated through event-driven simulations; and when possible, compared to the existing a-NET model [23].Dans ce travail, nous proposons des modèles approximatifs pour évaluer les performances d'un réseau de caches ayant une topologie arbitraire où les noeuds exécutent les politiques Least Recently Used (LRU), First In First Out (FIFO), ou Random replacement (RND) sur des caches de taille quelconque. Notre modèle tire parti des notions de temps caractéristique d'un cache et des modèles Time-To-Live (TTL) de cache pour développer une approche unifiée pour l'approximation des métriques de performance sur des caches interconnectés. Notre approche est validée par des simulations événementielles; et, si possible, comparée au modèle existant a-NET [23]
An approximate analysis of heterogeneous and general cache networks
In this paper, we propose approximate models to assess the performance of a cache network with arbitrary topology where nodes run the Least Recently Used (LRU), First-In First-Out (FIFO), or Random (RND) replacement policies on arbitrary size caches. Our model takes advantage of the notions of cache characteristic time and Time-To-Live (TTL)-based cache to develop a unified framework for approximating metrics of interest of interconnected caches. Our approach is validated through event-driven simulations; and when possible, compared to the existing a-NET model [23].Dans ce travail, nous proposons des modèles approximatifs pour évaluer les performances d'un réseau de caches ayant une topologie arbitraire où les noeuds exécutent les politiques Least Recently Used (LRU), First In First Out (FIFO), ou Random replacement (RND) sur des caches de taille quelconque. Notre modèle tire parti des notions de temps caractéristique d'un cache et des modèles Time-To-Live (TTL) de cache pour développer une approche unifiée pour l'approximation des métriques de performance sur des caches interconnectés. Notre approche est validée par des simulations événementielles; et, si possible, comparée au modèle existant a-NET [23]
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