N-gram based secure similar document detection

Wei Jiang; Bharath K. Samanthula

doi:10.1007/978-3-642-22348-8_19

N-gram based secure similar document detection

Wei Jiang, Bharath K. Samanthula

Computer Science

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

18 Scopus citations

Abstract

Secure similar document detection (SSDD) plays an important role in many applications, such as justifying the need-to-know basis and facilitating communication between government agencies. The SSDD problem considers situations where Alice with a query document wants to find similar information from Bob's document collection. During this process, the content of the query document is not disclosed to Bob, and Bob's document collection is not disclosed to Alice. Existing SSDD protocols are developed under the vector space model, which has the advantage of identifying global similar information. To effectively and securely detect similar documents with overlapping text fragments, this paper proposes a novel n-gram based SSDD protocol.

Original language	English
Title of host publication	Data and Applications Security and Privacy XXV - 25th Annual IFIP WG 11.3 Conference, DBSec 2011, Proceedings
Pages	239-246
Number of pages	8
DOIs	https://doi.org/10.1007/978-3-642-22348-8_19
State	Published - 2011
Event	25th Annual WG 11.3 Conference on Data and Applications Security and Privacy, DBSec 2011 - Richmond, VA, United States Duration: 11 Jul 2011 → 13 Jul 2011

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	6818 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	25th Annual WG 11.3 Conference on Data and Applications Security and Privacy, DBSec 2011
Country/Territory	United States
City	Richmond, VA
Period	11/07/11 → 13/07/11

Keywords

n-gram
privacy
security

Access to Document

10.1007/978-3-642-22348-8_19

Cite this

Jiang, W., & Samanthula, B. K. (2011). N-gram based secure similar document detection. In Data and Applications Security and Privacy XXV - 25th Annual IFIP WG 11.3 Conference, DBSec 2011, Proceedings (pp. 239-246). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6818 LNCS). https://doi.org/10.1007/978-3-642-22348-8_19

@inproceedings{387c08e2eb7043aa84a08765fc04a123,

title = "N-gram based secure similar document detection",

abstract = "Secure similar document detection (SSDD) plays an important role in many applications, such as justifying the need-to-know basis and facilitating communication between government agencies. The SSDD problem considers situations where Alice with a query document wants to find similar information from Bob's document collection. During this process, the content of the query document is not disclosed to Bob, and Bob's document collection is not disclosed to Alice. Existing SSDD protocols are developed under the vector space model, which has the advantage of identifying global similar information. To effectively and securely detect similar documents with overlapping text fragments, this paper proposes a novel n-gram based SSDD protocol.",

keywords = "n-gram, privacy, security",

author = "Wei Jiang and Samanthula, {Bharath K.}",

year = "2011",

doi = "10.1007/978-3-642-22348-8_19",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "239--246",

booktitle = "Data and Applications Security and Privacy XXV - 25th Annual IFIP WG 11.3 Conference, DBSec 2011, Proceedings",

note = "25th Annual WG 11.3 Conference on Data and Applications Security and Privacy, DBSec 2011 ; Conference date: 11-07-2011 Through 13-07-2011",

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Jiang, W & Samanthula, BK 2011, N-gram based secure similar document detection. in Data and Applications Security and Privacy XXV - 25th Annual IFIP WG 11.3 Conference, DBSec 2011, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6818 LNCS, pp. 239-246, 25th Annual WG 11.3 Conference on Data and Applications Security and Privacy, DBSec 2011, Richmond, VA, United States, 11/07/11. https://doi.org/10.1007/978-3-642-22348-8_19

N-gram based secure similar document detection. / Jiang, Wei; Samanthula, Bharath K.
Data and Applications Security and Privacy XXV - 25th Annual IFIP WG 11.3 Conference, DBSec 2011, Proceedings. 2011. p. 239-246 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6818 LNCS).

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

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AB - Secure similar document detection (SSDD) plays an important role in many applications, such as justifying the need-to-know basis and facilitating communication between government agencies. The SSDD problem considers situations where Alice with a query document wants to find similar information from Bob's document collection. During this process, the content of the query document is not disclosed to Bob, and Bob's document collection is not disclosed to Alice. Existing SSDD protocols are developed under the vector space model, which has the advantage of identifying global similar information. To effectively and securely detect similar documents with overlapping text fragments, this paper proposes a novel n-gram based SSDD protocol.

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BT - Data and Applications Security and Privacy XXV - 25th Annual IFIP WG 11.3 Conference, DBSec 2011, Proceedings

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Jiang W, Samanthula BK. N-gram based secure similar document detection. In Data and Applications Security and Privacy XXV - 25th Annual IFIP WG 11.3 Conference, DBSec 2011, Proceedings. 2011. p. 239-246. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-22348-8_19

N-gram based secure similar document detection

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