@inproceedings{e3b35c4263df475d96158b4bcab15909,
title = "Finding geometric medians with location privacy",
abstract = "We examine the problem of discovering the set P of points in a given topology which constitutes a k-median set for that topology, while maintaining location privacy. That is, there exists a set U of points in a d-dimensional topology for which a k-median set must be found by some algorithm A, without disclosing the location of points in U to the executor of A. We define a privacy preserving data model for a coordinate system we call a 'Topology Descriptor Grid', and show how it can be used to find the rectilinear 1-median of the system and a constant factor approximation for the Euclidean 1-median. Additionally, we achieve a constant factor approximation for the rectilinear 2-median of a grid topology.",
keywords = "Approximation, K-median, Location Privacy, Privacy, Rectilinear Median",
author = "Eyal Nussbaum and Michael Segal",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 19th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2020 ; Conference date: 29-12-2020 Through 01-01-2021",
year = "2020",
month = dec,
day = "1",
doi = "https://doi.org/10.1109/TrustCom50675.2020.00256",
language = "American English",
series = "Proceedings - 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2020",
pages = "1874--1881",
editor = "Guojun Wang and Ryan Ko and Bhuiyan, {Md Zakirul Alam} and Yi Pan",
booktitle = "Proceedings - 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2020",
}