Biblio

As the amount of spatial data gets bigger, organizations realized that it is cheaper and more flexible to keep their data on the Cloud rather than to establish and maintain in-house huge data centers. Though this saves a lot for IT costs, organizations are still concerned about the privacy and security of their data. Encrypting the whole database before uploading it to the Cloud solves the security issue. But querying the database requires downloading and decrypting the data set, which is impractical. In this paper, we propose a new scheme for protecting the privacy and integrity of spatial data stored in the Cloud while being able to execute range queries efficiently. The proposed technique suggests a new index structure to support answering range query over encrypted data set. The proposed indexing scheme is based on the Z-curve. The paper describes a distributed algorithm for answering range queries over spatial data stored on the Cloud. We carried many simulation experiments to measure the performance of the proposed scheme. The experimental results show that the proposed scheme outperforms the most recent schemes by Kim et al. in terms of data redundancy.

Modern storage systems stripe redundant data across multiple nodes to provide availability guarantees against node failures. One form of data redundancy is based on XOR-based erasure codes, which use only XOR operations for encoding and decoding. In addition to tolerating failures, a storage system must also provide fast failure recovery to reduce the window of vulnerability. This work addresses the problem of speeding up the recovery of a single-node failure for general XOR-based erasure codes. We propose a replace recovery algorithm, which uses a hill-climbing technique to search for a fast recovery solution, such that the solution search can be completed within a short time period. We further extend the algorithm to adapt to the scenario where nodes have heterogeneous capabilities (e.g., processing power and transmission bandwidth). We implement our replace recovery algorithm atop a parallelized architecture to demonstrate its feasibility. We conduct experiments on a networked storage system testbed, and show that our replace recovery algorithm uses less recovery time than the conventional recovery approach.