Abstract
Privacy and security concerns can prevent sharing of data, derailing many data projects. Distributed knowledge computing, if done correctly, can alleviate this problem. The key is to obtain valid results, while providing guarantees on the (non)disclosure of data. In the present study, two novel algorithms have been developed to find the nearest neighbor from known points, and a pair of nearest neighbors in a given set data points in d-dimensional space in distributed databases. These algorithms preserve the privacy and security of the data at individual sites by requiring transmission of only minimal information to other sites. (c) 2008 Elsevier Inc. All rights reserved.