Neighborhood-privacy protected shortest distance computing in cloud

Authors:
Jun Gao;Jeffrey Xu Yu;Ruoming Jin;Jiashuai Zhou;Tengjiao Wang;Dongqing Yang
Affiliations:
Peking University, Beijing, China;Chinese University of Hong Kong, Hong Kong, China;Kent State University, Ohio , USA;Peking University, Beijing, China;Peking University, Beijing, China;Peking University, Beijing, China
Venue:
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Year:
2011

Citing 21
Cited 4

Approximate distance oracles

STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Reachability and distance queries via 2-hop labels

SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
Hierarchical Encoded Path Views for Path Query Processing: An Optimal Model and Its Performance Evaluation

IEEE Transactions on Knowledge and Data Engineering
An Efficient Path Computation Model for Hierarchically Structured Topographical Road Maps

IEEE Transactions on Knowledge and Data Engineering
Providing Database as a Service

ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Computing the shortest path: A search meets graph theory

SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
Wherefore art thou r3579x?: anonymized social networks, hidden patterns, and structural steganography

Proceedings of the 16th international conference on World Wide Web
Towards identity anonymization on graphs

Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Resisting structural re-identification in anonymized social networks

Proceedings of the VLDB Endowment
Anonymizing bipartite graph data using safe groupings

Proceedings of the VLDB Endowment
Preserving Privacy in Social Networks Against Neighborhood Attacks

ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Secure outsourced aggregation via one-way chains

Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Triangulation and embedding using small sets of beacons

Journal of the ACM (JACM)
Fast shortest path distance estimation in large networks

Proceedings of the 18th ACM conference on Information and knowledge management
Accurate Estimation of the Degree Distribution of Private Networks

ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Class-based graph anonymization for social network data

Proceedings of the VLDB Endowment
Distance-join: pattern match query in a large graph database

Proceedings of the VLDB Endowment
k-automorphism: a general framework for privacy preserving network publication

Proceedings of the VLDB Endowment
Optimizing linear counting queries under differential privacy

Proceedings of the twenty-ninth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
K-isomorphism: privacy preserving network publication against structural attacks

Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Calibrating noise to sensitivity in private data analysis

TCC'06 Proceedings of the Third conference on Theory of Cryptography

Shortest path computation with no information leakage

Proceedings of the VLDB Endowment
Efficient breadth-first search on large graphs with skewed degree distributions

Proceedings of the 16th International Conference on Extending Database Technology
Outsourcing shortest distance computing with privacy protection

The VLDB Journal — The International Journal on Very Large Data Bases
Sensitive and Neighborhood Privacy on Shortest Paths in the Cloud

Proceedings of International Conference on Information Integration and Web-based Applications & Services

Quantified Score

Hi-index	0.00

Visualization

Abstract

With the advent of cloud computing, it becomes desirable to utilize cloud computing to efficiently process complex operations on large graphs without compromising their sensitive information. This paper studies shortest distance computing in the cloud, which aims at the following goals: i) preventing outsourced graphs from neighborhood attack, ii) preserving shortest distances in outsourced graphs, iii) minimizing overhead on the client side. The basic idea of this paper is to transform an original graph G into a link graph Gl kept locally and a set of outsourced graphs Go. Each outsourced graph should meet the requirement of a new security model called 1-neighborhood-d-radius. In addition, the shortest distance query can be answered using Gl and Go. Our objective is to minimize the space cost on the client side when both security and utility requirements are satisfied. We devise a greedy method to produce Gl and Go, which can exactly answer the shortest distance queries. We also develop an efficient transformation method to support approximate shortest distance answering under a given additive error bound. The final experimental results illustrate the effectiveness and efficiency of our method.