The amount of data processed annually over the Internet has crossed the zetabyte boundary, yet this Big Data cannot be efficiently processed or stored using today's mobile devices. Parallel to this explosive growth in data, a substantial increase in mobile compute-capability and the advances in cloud computing have brought the state-ofthe-art in mobile-cloud computing to an inflection point, where the right architecture may allow mobile devices to run applications utilizing Big Data and intensive computing. In this paper, we propose the MObile Cloud-based Hybrid Architecture (MOCHA), which formulates a solution to permit mobile-cloud computing applications such as object recognition in the battlefield by introducing a mid-stage compute-and storage-layer, called the cloudlet. MOCHA is built on the key observation that many mobile-cloud applications have the following characteristics: 1) they are compute-intensive, requiring the compute-power of a supercomputer, and 2) they use Big Data, requiring a communications link to cloud-based database sources in near-real-time. In this paper, we describe the operation of MOCHA in battlefield applications, by formulating the aforementioned mobile and cloudlet to be housed within a soldier's vest and inside a military vehicle, respectively, and enabling access to the cloud through high latency satellite links. We provide simulations using the traditional mobile-cloud approach as well as utilizing MOCHA with a mid-stage cloudlet to quantify the utility of this architecture. We show that the MOCHA platform for mobile-cloud computing promises a future for critical battlefield applications that access Big Data, which is currently not possible using existing technology.
This paper presents the Generalized Randomized Iterate of a (regular) one-way function f and show that it can be used to build Universal One-Way Hash Function (UOWHF) families with O(n 2) key length. We then show that Shoup's technique for UOWHF domain extension can be used to improve the efficiency of the previous construction. We present the Reusable Generalized Randomized Iterate which consists of k ≥ n + 1 iterations of a regular one-way function composed at each iteration with a pairwise independent hash function, where we only use log k such hash functions, and we "schedule" them according to the same scheduling of Shoup's domain extension technique. The end result is a UOWHF construction from regular one-way functions with an O(n log n) key. These are the first such efficient constructions of UOWHF from regular one-way functions of unknown regularity. Finally we show that the Shoup's domain extension technique can also be used in lieu of derandomization techniques to improve the efficiency of PRGs and of hardness amplification constructions for regular one-way functions.
There are many incentives for healthcare providers to shift their datacenters to the cloud. However, privacy of patient health information is a major concern when processing medical data off-site. One possible solution is the use of Fully Homomorphic Encryption (FHE), but this solution is too slow for most applications. We present a technique that increases efficiency and parallelism for certain algorithms under FHE. Through simulations, we demonstrate that our method yields about 20x speedup in a sample application. This is a significant step towards practical FHE-based medical remote monitoring.Globecom 2014 Workshop -Cloud Computing Systems, Networks, and Applications 978-1-4799-7470-2/14/$31.00 ©2014 IEEE
Extending cloud computing to medical software, where the hospitals rent the software from the provider sounds like a natural evolution for cloud computing. One problem with cloud computing, though, is ensuring the medical data privacy in applications such as long term health monitoring. Previously proposed solutions based on Fully Homomorphic Encryption (FHE) completely eliminate privacy concerns, but are extremely slow to be practical. Our key proposition in this paper is a new approach to applying FHE into the data that is stored in the cloud. Instead of using the existing circuit-based programming models, we propose a solution based on Branching Programs. While this restricts the type of data elements that FHE can be applied to, it achieves dramatic speed-up as compared to traditional circuit-based methods. Our claims are proven with simulations applied to real ECG data.
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