On the Declassification of Confidential Documents

Abril, Daniel; Navarro‐Arribas, Guillermo; Torra, Vicenç

doi:10.1007/978-3-642-22589-5_22

Cited by 32 publications

(30 citation statements)

References 18 publications

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“…Daniel.et.al [9] proposed a system that uses trained classifiers to detect the sensitive entities. It consists of two tasks: private entity recognition and Private entity protection.…”

Section: Using Trained Classifiers To Identify Sensitive Termsmentioning

confidence: 99%

A Review on Text Sanitization

Vasudevan¹,

John²

2014

IJCA

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Information is essential for all purpose of activities such as research, business decision making, etc. In this internet technology age there is no scarcity of information also. But if the information reveals the identity of a person or if it discloses confidential matters, then such information is a serious threat to privacy. So before publishing or sharing documents, the sensitive information should be removed or masked. This is the major goal of Text sanitization. Several semi-automatic and automatic methods are used for identifying sensitive information and thereby sanitizing the document by removing such terms. This broadens the users using the document due to their lowered classification level and also privacy is preserved.

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“…Daniel.et.al [9] proposed a system that uses trained classifiers to detect the sensitive entities. It consists of two tasks: private entity recognition and Private entity protection.…”

Section: Using Trained Classifiers To Identify Sensitive Termsmentioning

confidence: 99%

A Review on Text Sanitization

Vasudevan¹,

John²

2014

IJCA

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“…Daniel et.al [17] proposed a system based on the use of trained classifiers to find the sensitive terms. They used the term private entity to refer an entity which reveals information about the correspondents (organization or individual) directly or indirectly associated with the document.…”

Section: Figure 1: Overall System Architecturementioning

confidence: 99%

“…To overcome the limitations in [17] Sanchez et.al [7] proposed a method based on information theory and using web as corpus. The information content metric is used for identifying the sensitive terms in a document.…”

Section: Figure 1: Overall System Architecturementioning

confidence: 99%

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Automatic Declassification of Textual Documents by Generalizing Sensitive Terms

Vasudevan¹,

John²

2014

IJCA

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With the advent of internet, large numbers of text documents are published and shared every day .Each of these documents is a collection of vast amount of information. Publically sharing of some of this information may affect the privacy of the document, if they are confidential information. So before document publishing, sanitization operations are performed on the document for preserving the privacy and inorder to retain the utility of the document. Various schemes were developed to solve this problem but most of them turned out to be domain specific and most of them didn't consider the presence of semantically correlated terms. This paper presents a generalized sanitization method that discovers the sensitive information based on the concept of information content. The proposed method removes the confidential information from the text document by first finding the independent sensitive terms. Then with the use of these sensitive terms the correlated terms that cause a disclosure threat are discovered. Again with the help of a generalization algorithm these sensitive and correlated terms with high disclosure risk are generalized.

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“…Freedom of Information (FOI) laws exist in many countries around the world, including the United Kingdom (UK) 1 and the United States of America (USA) 2 . FOI states that government documents should be open to the public.…”

Section: Introductionmentioning

confidence: 99%

Using Part-of-Speech N-grams for Sensitive-Text Classification

McDonald

Macdonald

Ounis

2015

Proceedings of the 2015 International Conference on the Theory of Information Retrieval

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Freedom of Information legislations in many western democracies, including the United Kingdom (UK) and the United States of America (USA), state that citizens have typically the right to access government documents. However, certain sensitive information is exempt from release into the public domain. For example, in the UK, FOIA Exemption 27 (International Relations) excludes the release of Information that might damage the interests of the UK abroad. Therefore, the process of reviewing government documents for sensitivity is essential to determine if a document must be redacted before it is archived, or closed until the information is no longer sensitive. With the increased volume of digital government documents in recent years, there is a need for new tools to assist the digital sensitivity review process. Therefore, in this paper we propose an automatic approach for identifying sensitive text in documents by measuring the amount of sensitivity in sequences of text. Using government documents reviewed by trained sensitivity reviewers, we focus on an aspect of FOIA Exemption 27 which can have a major impact on international relations, namely information supplied in confidence. We show that our approach leads to markedly increased recall of sensitive text, while achieving a very high level of precision, when compared to a baseline that has been shown to be effective at identifying sensitive text in other domains.

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On the Declassification of Confidential Documents

Cited by 32 publications

References 18 publications

A Review on Text Sanitization

A Review on Text Sanitization

Automatic Declassification of Textual Documents by Generalizing Sensitive Terms

Using Part-of-Speech N-grams for Sensitive-Text Classification

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