Privacy-Preserving k-Nearest Neighbour Query on Outsourced Database

2016 ◽  
pp. 181-197 ◽  
Author(s):  
Rui Xu ◽  
Kirill Morozov ◽  
Yanjiang Yang ◽  
Jianying Zhou ◽  
Tsuyoshi Takagi
Keyword(s):  
Privacy Preserving ◽  
Nearest Neighbour ◽  
Outsourced Database

A Privacy-Preserving Join on Outsourced Database

2011 ◽  
pp. 278-292 ◽  
Author(s):  
Sha Ma ◽  
Bo Yang ◽  
Kangshun Li ◽  
Feng Xia
Keyword(s):  
Privacy Preserving ◽  
Outsourced Database

scholarly journals Long White Cloud (LWC): A Practical and Privacy-Preserving Outsourced Database

2018 ◽  
pp. 41-55
Author(s):  
Shujie Cui ◽  
Ming Zhang ◽  
Muhammad Rizwan Asghar ◽  
Giovanni Russello
Keyword(s):  
Privacy Preserving ◽  
Outsourced Database

scholarly journals An accuracy-assured privacy-preserving recommender system for internet commerce

2015 ◽  
Vol 12 (4) ◽  
pp. 1307-1326 ◽  
Author(s):  
Zhigang Lu ◽  
Hong Shen
Keyword(s):  
Covariance Matrix ◽  
Private Information ◽  
Differential Privacy ◽  
Selection Process ◽  
Privacy Preserving ◽  
Probabilistic Methods ◽  
Nearest Neighbour ◽  
Internet Applications ◽  
Novel Approach ◽  
Recommendation Accuracy

Recommender systems, tool for predicting users? potential preferences by computing history data and users? interests, show an increasing importance in various Internet applications such as online shopping. As a well-known recommendation method, neighbourhood-based collaborative filtering has attracted considerable attentions recently. The risk of revealing users? private information during the process of filtering has attracted noticeable research interests. Among the current solutions, the probabilistic techniques have shown a powerful privacy preserving effect. The existing methods deploying probabilistic methods are in three categories, one [19] adds differential privacy noises in the covariance matrix; one [1] introduces the randomisation in the neighbour selection process; the other [29] applies differential privacy in both the neighbour selection process and covariance matrix. When facing the k Nearest Neighbour (kNN) attack, all the existing methods provide no data utility guarantee, for the introduction of global randomness. In this paper, to overcome the problem of recommendation accuracy loss, we propose a novel approach, Partitioned Probabilistic Neighbour Selection, to ensure a required prediction accuracy while maintaining high security against the kNN attack. We define the sum of k neighbours? similarity as the accuracy metric ?, the number of user partitions, across which we select the k neighbours, as the security metric ?. We generalise the k Nearest Neighbour attack to the ?k Nearest Neighbours attack. Differing from the existing approach that selects neighbours across the entire candidate list randomly, our method selects neighbours from each exclusive partition of size k with a decreasing probability. Theoretical and experimental analysis show that to provide an accuracy-assured recommendation, our Partitioned Probabilistic Neighbour Selection method yields a better trade-off between the recommendation accuracy and system security.

Privacy-Preserving Top-k Spatial Keyword Queries over Outsourced Database

2015 ◽  
pp. 589-608 ◽  
Author(s):  
Sen Su ◽  
Yiping Teng ◽  
Xiang Cheng ◽  
Yulong Wang ◽  
Guoliang Li
Keyword(s):  
Privacy Preserving ◽  
Outsourced Database

The structure of amorphous and polycrystalline materials using energy-filtered RDF analysis

1992 ◽  
Vol 50 (2) ◽  
pp. 1190-1191
Author(s):  
David Cockayne ◽  
David McKenzie
Keyword(s):  
Electron Diffraction ◽  
Diffraction Pattern ◽  
Density Function ◽  
Electron Diffraction Pattern ◽  
Polycrystalline Materials ◽  
Nearest Neighbour ◽  
X Ray ◽  
Selected Area Electron Diffraction ◽  
Reduced Density ◽  
System F

The technique of Electron Reduced Density Function (RDF) analysis has ben developed into a rapid analytical tool for the analysis of small volumes of amorphous or polycrystalline materials. The energy filtered electron diffraction pattern is collected to high scattering angles (currendy to s = 2 sinθ/λ = 6.5 Å-1) by scanning the selected area electron diffraction pattern across the entrance aperture to a GATAN parallel energy loss spectrometer. The diffraction pattern is then converted to a reduced density function, G(r), using mathematical procedures equivalent to those used in X-ray and neutron diffraction studies.Nearest neighbour distances accurate to 0.01 Å are obtained routinely, and bond distortions of molecules can be determined from the ratio of first to second nearest neighbour distances. The accuracy of coordination number determinations from polycrystalline monatomic materials (eg Pt) is high (5%). In amorphous systems (eg carbon, silicon) it is reasonable (10%), but in multi-element systems there are a number of problems to be overcome; to reduce the diffraction pattern to G(r), the approximation must be made that for all elements i,j in the system, fj(s) = Kji fi,(s) where Kji is independent of s.

The Distribution of Hospitality Services in Uyo Urban, Nigeria

2018 ◽  
pp. 137-150
Author(s):  
Violet Bassey Eneyo
Keyword(s):  
Data Collection ◽  
Global Positioning System ◽  
Central Area ◽  
Positioning System ◽  
Nearest Neighbour ◽  
Global Positioning ◽  
Neighbour Analysis

This paper examines the distribution of hospitality services in Uyo Urban, Nigeria. GIS method was the primary tool used for data collection. A global positioning system (GPS) Garmin 60 model was used in tracking the location of 102 hospitality services in the study area. One hypothesis was stated and tested using the nearest neighbour analysis. The finding shows evidence of clustering of the various hospitality services. The tested hypothesis further indicated that hospitality services clustered in areas that guarantee a sustainable level of patronage to maximize profit. Thus, the hospitality services clustered in selected streets in the metropolis while limited numbers were found outside the city’s central area.

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