k-TTP: A new privacy model for large-scale distributed environments

Bobi Gilburd; Assaf Schuster; Ran Wolff

k-TTP: A new privacy model for large-scale distributed environments

Bobi Gilburd, Assaf Schuster, Ran Wolff

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

Abstract

Secure multiparty computation allows parties to jointly compute a function of their private inputs without revealing anything but the output. Theoretical results [2] provide a general construction of such protocols for any function. Protocols obtained in this way are, however, inefficient, and thus, practically speaking, useless when a large number of participants are involved. The contribution of this paper is to define a new privacy model - k-privacy - by means of an innovative, yet natural generalization of the accepted trusted third party model. This allows implementing cryptographically secure efficient primitives for real-world large-scale distributed systems. As an example for the usefulness of the proposed model, we employ k-privacy to introduce a technique for obtaining knowledge - by way of an association-rule mining algorithm - from large-scale Data Grids, while ensuring that the privacy is cryptographically secure.

Original language	English
Title of host publication	KDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Editors	R. Kohavi, J. Gehrke, W. DuMouchel, J. Ghosh
Pages	563-568
Number of pages	6
State	Published - 2004
Externally published	Yes
Event	KDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - Seattle, WA, United States Duration: 22 Aug 2004 → 25 Aug 2004

Publication series

Name	KDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference	KDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Country/Territory	United States
City	Seattle, WA
Period	22/08/04 → 25/08/04

Keywords

Association rule mining
Data mining
Distributed data mining
Privacy
Privacy-preserving data mining
Security

ASJC Scopus subject areas

General Engineering

Cite this

Gilburd, B., Schuster, A., & Wolff, R. (2004). k-TTP: A new privacy model for large-scale distributed environments. In R. Kohavi, J. Gehrke, W. DuMouchel, & J. Ghosh (Eds.), KDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 563-568). (KDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

Gilburd, Bobi ; Schuster, Assaf ; Wolff, Ran. / k-TTP : A new privacy model for large-scale distributed environments. KDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. editor / R. Kohavi ; J. Gehrke ; W. DuMouchel ; J. Ghosh. 2004. pp. 563-568 (KDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

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title = "k-TTP: A new privacy model for large-scale distributed environments",

abstract = "Secure multiparty computation allows parties to jointly compute a function of their private inputs without revealing anything but the output. Theoretical results [2] provide a general construction of such protocols for any function. Protocols obtained in this way are, however, inefficient, and thus, practically speaking, useless when a large number of participants are involved. The contribution of this paper is to define a new privacy model - k-privacy - by means of an innovative, yet natural generalization of the accepted trusted third party model. This allows implementing cryptographically secure efficient primitives for real-world large-scale distributed systems. As an example for the usefulness of the proposed model, we employ k-privacy to introduce a technique for obtaining knowledge - by way of an association-rule mining algorithm - from large-scale Data Grids, while ensuring that the privacy is cryptographically secure.",

keywords = "Association rule mining, Data mining, Distributed data mining, Privacy, Privacy-preserving data mining, Security",

author = "Bobi Gilburd and Assaf Schuster and Ran Wolff",

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editor = "R. Kohavi and J. Gehrke and W. DuMouchel and J. Ghosh",

booktitle = "KDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",

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Gilburd, B, Schuster, A & Wolff, R 2004, k-TTP: A new privacy model for large-scale distributed environments. in R Kohavi, J Gehrke, W DuMouchel & J Ghosh (eds), KDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 563-568, KDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Seattle, WA, United States, 22/08/04.

k-TTP: A new privacy model for large-scale distributed environments. / Gilburd, Bobi; Schuster, Assaf; Wolff, Ran.
KDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ed. / R. Kohavi; J. Gehrke; W. DuMouchel; J. Ghosh. 2004. p. 563-568 (KDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

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

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AU - Schuster, Assaf

AU - Wolff, Ran

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N2 - Secure multiparty computation allows parties to jointly compute a function of their private inputs without revealing anything but the output. Theoretical results [2] provide a general construction of such protocols for any function. Protocols obtained in this way are, however, inefficient, and thus, practically speaking, useless when a large number of participants are involved. The contribution of this paper is to define a new privacy model - k-privacy - by means of an innovative, yet natural generalization of the accepted trusted third party model. This allows implementing cryptographically secure efficient primitives for real-world large-scale distributed systems. As an example for the usefulness of the proposed model, we employ k-privacy to introduce a technique for obtaining knowledge - by way of an association-rule mining algorithm - from large-scale Data Grids, while ensuring that the privacy is cryptographically secure.

AB - Secure multiparty computation allows parties to jointly compute a function of their private inputs without revealing anything but the output. Theoretical results [2] provide a general construction of such protocols for any function. Protocols obtained in this way are, however, inefficient, and thus, practically speaking, useless when a large number of participants are involved. The contribution of this paper is to define a new privacy model - k-privacy - by means of an innovative, yet natural generalization of the accepted trusted third party model. This allows implementing cryptographically secure efficient primitives for real-world large-scale distributed systems. As an example for the usefulness of the proposed model, we employ k-privacy to introduce a technique for obtaining knowledge - by way of an association-rule mining algorithm - from large-scale Data Grids, while ensuring that the privacy is cryptographically secure.

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Gilburd B, Schuster A, Wolff R. k-TTP: A new privacy model for large-scale distributed environments. In Kohavi R, Gehrke J, DuMouchel W, Ghosh J, editors, KDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2004. p. 563-568. (KDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

k-TTP: A new privacy model for large-scale distributed environments

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

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