Privacy-preserving network aggregation

Troy Raeder; Marina Blanton; Nitesh V. Chawla; Keith Frikken

doi:10.1007/978-3-642-13657-3_23

Privacy-preserving network aggregation

Troy Raeder
, Marina Blanton
, Nitesh V. Chawla
, Keith Frikken

Department of Computer Science and Engineering

University of Notre Dame
Miami University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

Consider the scenario where information about a large network is distributed across several different parties or commercial entities. Intuitively, we would expect that the aggregate network formed by combining the individual private networks would be a more faithful representation of the network phenomenon as a whole. However, privacy preservation of the individual networks becomes a mandate. Thus, it would be useful, given several portions of an underlying network p1... pn, to securely compute the aggregate of all the networks pi in a manner such that no party learns information about any other party's network. In this work, we propose a novel privacy preservation protocol for the non-trivial case of weighted networks. The protocol is secure against malicious adversaries.

Original language	English
Title of host publication	Advances in Knowledge Discovery and Data Mining - 14th Pacific-Asia Conference, PAKDD 2010, Proceedings
Pages	198-207
Number of pages	10
Edition	PART 1
DOIs	https://doi.org/10.1007/978-3-642-13657-3_23
State	Published - 2010
Event	14th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2010 - Hyderabad, India Duration: Jun 21 2010 → Jun 24 2010

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	6118 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	14th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2010
Country/Territory	India
City	Hyderabad
Period	06/21/10 → 06/24/10

Access to Document

10.1007/978-3-642-13657-3_23

Cite this

Raeder, T., Blanton, M., Chawla, N. V., & Frikken, K. (2010). Privacy-preserving network aggregation. In Advances in Knowledge Discovery and Data Mining - 14th Pacific-Asia Conference, PAKDD 2010, Proceedings (PART 1 ed., pp. 198-207). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6118 LNAI, No. PART 1). https://doi.org/10.1007/978-3-642-13657-3_23

Raeder, Troy ; Blanton, Marina ; Chawla, Nitesh V. et al. / Privacy-preserving network aggregation. Advances in Knowledge Discovery and Data Mining - 14th Pacific-Asia Conference, PAKDD 2010, Proceedings. PART 1. ed. 2010. pp. 198-207 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

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title = "Privacy-preserving network aggregation",

abstract = "Consider the scenario where information about a large network is distributed across several different parties or commercial entities. Intuitively, we would expect that the aggregate network formed by combining the individual private networks would be a more faithful representation of the network phenomenon as a whole. However, privacy preservation of the individual networks becomes a mandate. Thus, it would be useful, given several portions of an underlying network p1... pn, to securely compute the aggregate of all the networks pi in a manner such that no party learns information about any other party's network. In this work, we propose a novel privacy preservation protocol for the non-trivial case of weighted networks. The protocol is secure against malicious adversaries.",

author = "Troy Raeder and Marina Blanton and Chawla, \{Nitesh V.\} and Keith Frikken",

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Raeder, T, Blanton, M, Chawla, NV & Frikken, K 2010, Privacy-preserving network aggregation. in Advances in Knowledge Discovery and Data Mining - 14th Pacific-Asia Conference, PAKDD 2010, Proceedings. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 6118 LNAI, pp. 198-207, 14th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2010, Hyderabad, India, 06/21/10. https://doi.org/10.1007/978-3-642-13657-3_23

Privacy-preserving network aggregation. / Raeder, Troy; Blanton, Marina; Chawla, Nitesh V. et al.
Advances in Knowledge Discovery and Data Mining - 14th Pacific-Asia Conference, PAKDD 2010, Proceedings. PART 1. ed. 2010. p. 198-207 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6118 LNAI, No. PART 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - Consider the scenario where information about a large network is distributed across several different parties or commercial entities. Intuitively, we would expect that the aggregate network formed by combining the individual private networks would be a more faithful representation of the network phenomenon as a whole. However, privacy preservation of the individual networks becomes a mandate. Thus, it would be useful, given several portions of an underlying network p1... pn, to securely compute the aggregate of all the networks pi in a manner such that no party learns information about any other party's network. In this work, we propose a novel privacy preservation protocol for the non-trivial case of weighted networks. The protocol is secure against malicious adversaries.

AB - Consider the scenario where information about a large network is distributed across several different parties or commercial entities. Intuitively, we would expect that the aggregate network formed by combining the individual private networks would be a more faithful representation of the network phenomenon as a whole. However, privacy preservation of the individual networks becomes a mandate. Thus, it would be useful, given several portions of an underlying network p1... pn, to securely compute the aggregate of all the networks pi in a manner such that no party learns information about any other party's network. In this work, we propose a novel privacy preservation protocol for the non-trivial case of weighted networks. The protocol is secure against malicious adversaries.

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Raeder T, Blanton M, Chawla NV, Frikken K. Privacy-preserving network aggregation. In Advances in Knowledge Discovery and Data Mining - 14th Pacific-Asia Conference, PAKDD 2010, Proceedings. PART 1 ed. 2010. p. 198-207. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-642-13657-3_23

Privacy-preserving network aggregation

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