PrivateGraph: A Cloud-Centric System for Spectral Analysis of Large Encrypted Graphs

TitlePrivateGraph: A Cloud-Centric System for Spectral Analysis of Large Encrypted Graphs
Publication TypeConference Paper
Year of Publication2017
AuthorsSharma, S, Chen, K
Conference Name2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)
Pagination2507-2510
Date PublishedJune
Conference LocationAtlanta,GA,USA
ISSN Number1063-6927
KeywordsAlgorithm design and analysis, Cloud Computing, cloud-centric system, cloud-client interaction protocols, Clustering algorithms, cryptography, Data privacy, distributed databases, eigen-decomposition algorithms, eigenvalues and eigenfunctions, encrypted data, framework scalability, large encrypted graphs, privacy-preserving data submission, PrivateGraph, result quality, spectral analysis
Abstract

Graph datasets have invaluable use in business applications and scientific research. Because of the growing size and dynamically changing nature of graphs, graph data owners may want to use public cloud infrastructures to store, process, and perform graph analytics. However, when outsourcing data and computation, data owners are at burden to develop methods to preserve data privacy and data ownership from curious cloud providers. This demonstration exhibits a prototype system for privacy-preserving spectral analysis framework for large graphs in public clouds (PrivateGraph) that allows data owners to collect graph data from data contributors, and store and conduct secure graph spectral analysis in the cloud with preserved privacy and ownership. This demo system lets its audience interactively learn the major cloud-client interaction protocols: the privacy-preserving data submission, the secure Lanczos and Nyström approximate eigen-decomposition algorithms that work over encrypted data, and the outcome of an important application of spectral analysis - spectral clustering. In the process of demonstration the audience will understand the intrinsic relationship amongst costs, result quality, privacy, and scalability of the framework.

DOI10.1109/ICDCS.2017.189
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