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Title: Cyber Graph Queries for Geographically Distributed Data Centers

Abstract

We present new algorithms for a distributed model for graph computations motivated by limited information sharing we first discussed in [20]. Two or more independent entities have collected large social graphs. They wish to compute the result of running graph algorithms on the entire set of relationships. Because the information is sensitive or economically valuable, they do not wish to simply combine the information in a single location. We consider two models for computing the solution to graph algorithms in this setting: 1) limited-sharing: the two entities can share only a polylogarithmic size subgraph; 2) low-trust: the entities must not reveal any information beyond the query answer, assuming they are all honest but curious. We believe this model captures realistic constraints on cooperating autonomous data centers. We have algorithms in both setting for s - t connectivity in both models. We also give an algorithm in the low-communication model for finding a planted clique. This is an anomaly- detection problem, finding a subgraph that is larger and denser than expected. For both the low- communication algorithms, we exploit structural properties of social networks to prove perfor- mance bounds better than what is possible for general graphs. For s - tmore » connectivity, we use known properties. For planted clique, we propose a new property: bounded number of triangles per node. This property is based upon evidence from the social science literature. We found that classic examples of social networks do not have the bounded-triangles property. This is because many social networks contain elements that are non-human, such as accounts for a business, or other automated accounts. We describe some initial attempts to distinguish human nodes from automated nodes in social networks based only on topological properties.« less

Authors:
 [1];  [2];  [3];  [1];  [3]
  1. Mail Stop, Albuquerque, NM (United States)
  2. Christopher Newport Univ., VA (United States)
  3. Univ. of New Mexico, Albuquerque, NM (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1183059
Report Number(s):
SAND2015-4087
584065
DOE Contract Number:  
AC04-94AL85000
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English

Citation Formats

Berry, Jonathan W., Collins, Michael, Kearns, Aaron, Phillips, Cynthia A., and Saia, Jared. Cyber Graph Queries for Geographically Distributed Data Centers. United States: N. p., 2015. Web. doi:10.2172/1183059.
Berry, Jonathan W., Collins, Michael, Kearns, Aaron, Phillips, Cynthia A., & Saia, Jared. Cyber Graph Queries for Geographically Distributed Data Centers. United States. doi:10.2172/1183059.
Berry, Jonathan W., Collins, Michael, Kearns, Aaron, Phillips, Cynthia A., and Saia, Jared. Fri . "Cyber Graph Queries for Geographically Distributed Data Centers". United States. doi:10.2172/1183059. https://www.osti.gov/servlets/purl/1183059.
@article{osti_1183059,
title = {Cyber Graph Queries for Geographically Distributed Data Centers},
author = {Berry, Jonathan W. and Collins, Michael and Kearns, Aaron and Phillips, Cynthia A. and Saia, Jared},
abstractNote = {We present new algorithms for a distributed model for graph computations motivated by limited information sharing we first discussed in [20]. Two or more independent entities have collected large social graphs. They wish to compute the result of running graph algorithms on the entire set of relationships. Because the information is sensitive or economically valuable, they do not wish to simply combine the information in a single location. We consider two models for computing the solution to graph algorithms in this setting: 1) limited-sharing: the two entities can share only a polylogarithmic size subgraph; 2) low-trust: the entities must not reveal any information beyond the query answer, assuming they are all honest but curious. We believe this model captures realistic constraints on cooperating autonomous data centers. We have algorithms in both setting for s - t connectivity in both models. We also give an algorithm in the low-communication model for finding a planted clique. This is an anomaly- detection problem, finding a subgraph that is larger and denser than expected. For both the low- communication algorithms, we exploit structural properties of social networks to prove perfor- mance bounds better than what is possible for general graphs. For s - t connectivity, we use known properties. For planted clique, we propose a new property: bounded number of triangles per node. This property is based upon evidence from the social science literature. We found that classic examples of social networks do not have the bounded-triangles property. This is because many social networks contain elements that are non-human, such as accounts for a business, or other automated accounts. We describe some initial attempts to distinguish human nodes from automated nodes in social networks based only on topological properties.},
doi = {10.2172/1183059},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2015},
month = {5}
}

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