Publishing attributed social graphs with formal privacy guarantees

Zach Jorgensen, Ting Yu, Graham Cormode

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)

Abstract

Many data analysis tasks rely on the abstraction of a graph to represent relations between entities, with attributes on the nodes and edges. Since the relationships encoded are often sensitive, we seek effective ways to release representative graphs which nevertheless protect the privacy of the data subjects. Prior work on this topic has focused primarily on the graph structure in isolation, and has not provided ways to handle richer graphs with correlated attributes. We introduce an approach to release such graphs under the strong guarantee of differential privacy. We adapt existing graph models, and introduce a new one, and show how to augment them with meaningful privacy. This provides a complete workflow, where the input is a sensitive graph, and the output is a realistic synthetic graph. Our experimental study demonstrates that our process produces useful, accurate attributed graphs.

Original languageEnglish
Title of host publicationSIGMOD 2016 - Proceedings of the 2016 International Conference on Management of Data
PublisherAssociation for Computing Machinery
Pages107-122
Number of pages16
Volume26-June-2016
ISBN (Electronic)9781450335317
DOIs
Publication statusPublished - 26 Jun 2016
Event2016 ACM SIGMOD International Conference on Management of Data, SIGMOD 2016 - San Francisco, United States
Duration: 26 Jun 20161 Jul 2016

Other

Other2016 ACM SIGMOD International Conference on Management of Data, SIGMOD 2016
CountryUnited States
CitySan Francisco
Period26/6/161/7/16

ASJC Scopus subject areas

  • Software
  • Information Systems

Cite this

Jorgensen, Z., Yu, T., & Cormode, G. (2016). Publishing attributed social graphs with formal privacy guarantees. In SIGMOD 2016 - Proceedings of the 2016 International Conference on Management of Data (Vol. 26-June-2016, pp. 107-122). Association for Computing Machinery. https://doi.org/10.1145/2882903.2915215

Publishing attributed social graphs with formal privacy guarantees. / Jorgensen, Zach; Yu, Ting; Cormode, Graham.

SIGMOD 2016 - Proceedings of the 2016 International Conference on Management of Data. Vol. 26-June-2016 Association for Computing Machinery, 2016. p. 107-122.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Jorgensen, Z, Yu, T & Cormode, G 2016, Publishing attributed social graphs with formal privacy guarantees. in SIGMOD 2016 - Proceedings of the 2016 International Conference on Management of Data. vol. 26-June-2016, Association for Computing Machinery, pp. 107-122, 2016 ACM SIGMOD International Conference on Management of Data, SIGMOD 2016, San Francisco, United States, 26/6/16. https://doi.org/10.1145/2882903.2915215
Jorgensen Z, Yu T, Cormode G. Publishing attributed social graphs with formal privacy guarantees. In SIGMOD 2016 - Proceedings of the 2016 International Conference on Management of Data. Vol. 26-June-2016. Association for Computing Machinery. 2016. p. 107-122 https://doi.org/10.1145/2882903.2915215
Jorgensen, Zach ; Yu, Ting ; Cormode, Graham. / Publishing attributed social graphs with formal privacy guarantees. SIGMOD 2016 - Proceedings of the 2016 International Conference on Management of Data. Vol. 26-June-2016 Association for Computing Machinery, 2016. pp. 107-122
@inproceedings{9c361177d51a404689c08402eaf20a45,
title = "Publishing attributed social graphs with formal privacy guarantees",
abstract = "Many data analysis tasks rely on the abstraction of a graph to represent relations between entities, with attributes on the nodes and edges. Since the relationships encoded are often sensitive, we seek effective ways to release representative graphs which nevertheless protect the privacy of the data subjects. Prior work on this topic has focused primarily on the graph structure in isolation, and has not provided ways to handle richer graphs with correlated attributes. We introduce an approach to release such graphs under the strong guarantee of differential privacy. We adapt existing graph models, and introduce a new one, and show how to augment them with meaningful privacy. This provides a complete workflow, where the input is a sensitive graph, and the output is a realistic synthetic graph. Our experimental study demonstrates that our process produces useful, accurate attributed graphs.",
author = "Zach Jorgensen and Ting Yu and Graham Cormode",
year = "2016",
month = "6",
day = "26",
doi = "10.1145/2882903.2915215",
language = "English",
volume = "26-June-2016",
pages = "107--122",
booktitle = "SIGMOD 2016 - Proceedings of the 2016 International Conference on Management of Data",
publisher = "Association for Computing Machinery",

}

TY - GEN

T1 - Publishing attributed social graphs with formal privacy guarantees

AU - Jorgensen, Zach

AU - Yu, Ting

AU - Cormode, Graham

PY - 2016/6/26

Y1 - 2016/6/26

N2 - Many data analysis tasks rely on the abstraction of a graph to represent relations between entities, with attributes on the nodes and edges. Since the relationships encoded are often sensitive, we seek effective ways to release representative graphs which nevertheless protect the privacy of the data subjects. Prior work on this topic has focused primarily on the graph structure in isolation, and has not provided ways to handle richer graphs with correlated attributes. We introduce an approach to release such graphs under the strong guarantee of differential privacy. We adapt existing graph models, and introduce a new one, and show how to augment them with meaningful privacy. This provides a complete workflow, where the input is a sensitive graph, and the output is a realistic synthetic graph. Our experimental study demonstrates that our process produces useful, accurate attributed graphs.

AB - Many data analysis tasks rely on the abstraction of a graph to represent relations between entities, with attributes on the nodes and edges. Since the relationships encoded are often sensitive, we seek effective ways to release representative graphs which nevertheless protect the privacy of the data subjects. Prior work on this topic has focused primarily on the graph structure in isolation, and has not provided ways to handle richer graphs with correlated attributes. We introduce an approach to release such graphs under the strong guarantee of differential privacy. We adapt existing graph models, and introduce a new one, and show how to augment them with meaningful privacy. This provides a complete workflow, where the input is a sensitive graph, and the output is a realistic synthetic graph. Our experimental study demonstrates that our process produces useful, accurate attributed graphs.

UR - http://www.scopus.com/inward/record.url?scp=84979688598&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84979688598&partnerID=8YFLogxK

U2 - 10.1145/2882903.2915215

DO - 10.1145/2882903.2915215

M3 - Conference contribution

AN - SCOPUS:84979688598

VL - 26-June-2016

SP - 107

EP - 122

BT - SIGMOD 2016 - Proceedings of the 2016 International Conference on Management of Data

PB - Association for Computing Machinery

ER -