Implementation of Space-Filling Curves on Spatial Dataset: A Review Paper

Cloud computing is the most recent innovative achievement that everybody ought to know about independent of whether you are a provider or a purchaser of innovative technology. Financial benefits are the essential driver for the Cloud, since it ensures the diminishment of capital utilize and operational utilize. The widespread use of the cloud has lead to the rise of database outsourcing. Privacy and security are the main considerations in the database outsourcing. Most of the conventional approaches provide security to outsourced data either by existing cryptographic techniques or using spatial transformation schemes. Here we propose a system which will implement and compare two space-filling algorithms (Hilbert curve and Gosper curve) on spatial data

DESIGN AND IMPLEMENTATION OF RASP IN DATA COMMOTION IN BUILDING ARCANE AND CAPABILITY QUERY SERVICES

Because of distinctive advantages in scalability and price-saving, storing of understanding-intensive query services in cloud gets increasingly popular. We setup random space perturbation system to produce realistic range query and k nearest-neighbour services of query in cloud. The forecasted approach will undertake data confidentiality, privacy of query, efficient processing of query furthermore to lessen in-house price of processing, and obtain a great balance within it. Random space perturbation system is a kind of growing perturbation, by mixture of order preserving file encryption, random noise injection, and random project. Random space perturbation system encloses lots of significant features. The fundamental proposal should be to at random modify complex data sets by grouping of order preserving file encryption, random noise injection, random project and dimensionality expansion, to make sure that utility for handing range queries is preserved

A Review on Protecting Location Privacy for Task Allocation in Mobile Cloud Computing

Cloud computing has extensively been observed as the next-generation calculating example which provides limitless cloud resources to finale users in anon request manner. The amusing cloud resources in cloud figuring can be subjugated to upsurge, augment, and improve abilities of mobile devices, important to the thought of mobile cloud computing(MCC).We recommend a basis that affords explanations to the beyond contests, where together position concealment and package equality are measured. In our outline, the CCP only has contact to sanitized location data of mobile servers rendering to differential privacy (DP).Mean while each mobile server is pledged to a cellular service provider(CSP) with which it previously has a faith association, the CSP can assimilate mobile server position and standing information, and delivers the data to the CCP in deafening form according to DP. To produce the deafening mobile server data, we acclimate the Private Spatial Decomposition (PSD) method and paradigm a new assembly called Reputation-based PSD (R-PSD)

Secure kk-ish Nearest Neighbors Classifier

In machine learning, classifiers are used to predict a class of a given query based on an existing (classified) database. Given a database S of n d-dimensional points and a d-dimensional query q, the k-nearest neighbors (kNN) classifier assigns q with the majority class of its k nearest neighbors in S. In the secure version of kNN, S and q are owned by two different parties that do not want to share their data. Unfortunately, all known solutions for secure kNN either require a large communication complexity between the parties, or are very inefficient to run. In this work we present a classifier based on kNN, that can be implemented efficiently with homomorphic encryption (HE). The efficiency of our classifier comes from a relaxation we make on kNN, where we allow it to consider kappa nearest neighbors for kappa ~ k with some probability. We therefore call our classifier k-ish Nearest Neighbors (k-ish NN). The success probability of our solution depends on the distribution of the distances from q to S and increase as its statistical distance to Gaussian decrease. To implement our classifier we introduce the concept of double-blinded coin-toss. In a doubly-blinded coin-toss the success probability as well as the output of the toss are encrypted. We use this coin-toss to efficiently approximate the average and variance of the distances from q to S. We believe these two techniques may be of independent interest. When implemented with HE, the k-ish NN has a circuit depth that is independent of n, therefore making it scalable. We also implemented our classifier in an open source library based on HELib and tested it on a breast tumor database. The accuracy of our classifier (F_1 score) were 98\% and classification took less than 3 hours compared to (estimated) weeks in current HE implementations

Building Confidential and Efficient Query Services in the Cloud with RASP Data Perturbation

With the wide deployment of public cloud computing infrastructures, using clouds to host data query services has become an appealing solution for the advantages on scalability and cost-saving. However, some data might be sensitive that the data owner does not want to move to the cloud unless the data confidentiality and query privacy are guaranteed. On the other hand, a secured query service should still provide efficient query processing and significantly reduce the in-house workload to fully realize the benefits of cloud computing. We propose the RASP data perturbation method to provide secure and efficient range query and kNN query services for protected data in the cloud. The RASP data perturbation method combines order preserving encryption, dimensionality expansion, random noise injection, and random projection, to provide strong resilience to attacks on the perturbed data and queries. It also preserves multidimensional ranges, which allows existing indexing techniques to be applied to speedup range query processing. The kNN-R algorithm is designed to work with the RASP range query algorithm to process the kNN queries. We have carefully analyzed the attacks on data and queries under a precisely defined threat model and realistic security assumptions. Extensive experiments have been conducted to show the advantages of this approach on efficiency and security.Comment: 18 pages, to appear in IEEE TKDE, accepted in December 201

Shortest Path Computation with No Information Leakage

Shortest path computation is one of the most common queries in location-based services (LBSs). Although particularly useful, such queries raise serious privacy concerns. Exposing to a (potentially untrusted) LBS the client's position and her destination may reveal personal information, such as social habits, health condition, shopping preferences, lifestyle choices, etc. The only existing method for privacy-preserving shortest path computation follows the obfuscation paradigm; it prevents the LBS from inferring the source and destination of the query with a probability higher than a threshold. This implies, however, that the LBS still deduces some information (albeit not exact) about the client's location and her destination. In this paper we aim at strong privacy, where the adversary learns nothing about the shortest path query. We achieve this via established private information retrieval techniques, which we treat as black-box building blocks. Experiments on real, large-scale road networks assess the practicality of our schemes.Comment: VLDB201

Spatial Data Quality in the IoT Era:Management and Exploitation

Within the rapidly expanding Internet of Things (IoT), growing amounts of spatially referenced data are being generated. Due to the dynamic, decentralized, and heterogeneous nature of the IoT, spatial IoT data (SID) quality has attracted considerable attention in academia and industry. How to invent and use technologies for managing spatial data quality and exploiting low-quality spatial data are key challenges in the IoT. In this tutorial, we highlight the SID consumption requirements in applications and offer an overview of spatial data quality in the IoT setting. In addition, we review pertinent technologies for quality management and low-quality data exploitation, and we identify trends and future directions for quality-aware SID management and utilization. The tutorial aims to not only help researchers and practitioners to better comprehend SID quality challenges and solutions, but also offer insights that may enable innovative research and applications