Gonzalez-Manzano L.,Charles III University of Madrid |
Gonzalez-Manzano L.,Avenida Of La University 30 |
Orfila A.,Charles III University of Madrid |
Orfila A.,Avenida Of La University 30
Journal of Network and Computer Applications | Year: 2015
Data storage in the cloud is becoming widespread. Deduplication is a key mechanism to decrease the operating costs cloud providers face, due to the reduction of replicated data storage. Nonetheless, deduplication must deal with several security threats such as honest-but-curious servers or malicious users who may try to take ownership of files they are not entitled to. Unfortunately, state-of-the-art solutions present weaknesses such as not coping with honest-but-curious servers, deployment problems, or lacking a sound security analysis. In this paper we present a novel Proof of Ownership scheme that uses convergent encryption and requires neither trusted third parties nor complex key management. The experimental evaluation highlights the efficiency and feasibility of our proposal that is proven to be secure under the random oracle model in the bounded leakage setting. © 2015 Elsevier Ltd. All rights reserved.
Read J.,Avenida Of La University 30 |
Achutegui K.,Avenida Of La University 30 |
Miguez J.,Avenida Of La University 30
Signal Processing | Year: 2014
The use of distributed particle filters for tracking in sensor networks has become popular in recent years. The distributed particle filters proposed in the literature up to now are only approximations of the centralized particle filter or, if they are a proper distributed version of the particle filter, their implementation in a wireless sensor network demands a prohibitive communication capability. In this work, we propose a mathematically sound distributed particle filter for tracking in a real-world indoor wireless sensor network composed of low-power nodes. We provide formal and general descriptions of our methodology and then present the results of both real-world experiments and/or computer simulations that use models fitted with real data. With the same number of particles as a centralized filter, the distributed algorithm is over four times faster, yet our simulations show that, even assuming the same processing speed, the accuracy of the centralized and distributed algorithms is practically identical. The main limitation of the proposed scheme is the need to make all the sensor observations available to every processing node. Therefore, it is better suited to broadcast networks or multihop networks where the volume of generated data is kept low, e.g., by an adequate local pre-processing of the observations. © 2013 Elsevier B.V.