東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Mining roles and access control for ...

Pervaiz, Zahid.

Linked to FindBook

Google Book

Amazon

博客來

Mining roles and access control for relational data under privacy and accuracy constraints.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Mining roles and access control for relational data under privacy and accuracy constraints./
Author:	Pervaiz, Zahid.
Description:	137 p.
Notes:	Source: Dissertation Abstracts International, Volume: 74-12(E), Section: B.
Contained By:	Dissertation Abstracts International74-12B(E).
Subject:	Engineering, Computer. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3592081
ISBN:	9781303329432

Mining roles and access control for relational data under privacy and accuracy constraints.
Pervaiz, Zahid.

Mining roles and access control for relational data under privacy and accuracy constraints. - 137 p.

Source: Dissertation Abstracts International, Volume: 74-12(E), Section: B.

Thesis (Ph.D.)--Purdue University, 2013.

Access control mechanisms protect sensitive information from unauthorized users. However, when sensitive information is shared and a Privacy Protection Mechanism (PPM) is not in place, an authorized insider can still compromise the privacy of a person leading to identity disclosure. A PPM can use suppression and generalization to anonymize and satisfy privacy requirements, e.g., k-anonymity and l-diversity. However, the protection of privacy is achieved at the cost of precision of authorized information. In this thesis, we propose an accuracy-constrained privacy-preserving access control framework for static relational data and data streams. The access control policies define selection predicates available to roles and the associated imprecision bound. The PPM has to satisfy the privacy requirement along with the imprecision bound for each selection predicate. We prove the hardness of problem, propose heuristics for anonymization algorithms and show empirically that the proposed approach satisfies imprecision bounds for more queries than the current state of the art. We also formulate the problem of predicate role mining for extraction of authorized selection predicates and propose an approximate algorithm. The access control for stream data allows roles access to tuples satisfying an authorized predicate sliding window query. The generalization introduces imprecision in the authorized view of stream. This imprecision can be reduced by delaying the publishing of stream data. However, the delay in sharing the stream tuples to access control can lead to false negatives. The challenge is to optimize the time duration for which the data is held by PPM so that the imprecision bound for maximum number of queries are met. We present the hardness results, provide an anonymization algorithm, and conduct experimental evaluation of the proposed algorithm.

ISBN: 9781303329432Subjects--Topical Terms:

1669061
Engineering, Computer.

Mining roles and access control for relational data under privacy and accuracy constraints.
LDR:02778nam a2200289 4500 001 1963500
005 20141003074100.5
008 150210s2013 ||||||||||||||||| ||eng d
020 $a 9781303329432
035 $a (MiAaPQ)AAI3592081
035 $a AAI3592081
040 $a MiAaPQ $c MiAaPQ
100 1 $a Pervaiz, Zahid. $3 2099770
245 1 0 $a Mining roles and access control for relational data under privacy and accuracy constraints.
300 $a 137 p.
500 $a Source: Dissertation Abstracts International, Volume: 74-12(E), Section: B.
500 $a Adviser: Arif Ghafoor.
502 $a Thesis (Ph.D.)--Purdue University, 2013.
520 $a Access control mechanisms protect sensitive information from unauthorized users. However, when sensitive information is shared and a Privacy Protection Mechanism (PPM) is not in place, an authorized insider can still compromise the privacy of a person leading to identity disclosure. A PPM can use suppression and generalization to anonymize and satisfy privacy requirements, e.g., k-anonymity and l-diversity. However, the protection of privacy is achieved at the cost of precision of authorized information. In this thesis, we propose an accuracy-constrained privacy-preserving access control framework for static relational data and data streams. The access control policies define selection predicates available to roles and the associated imprecision bound. The PPM has to satisfy the privacy requirement along with the imprecision bound for each selection predicate. We prove the hardness of problem, propose heuristics for anonymization algorithms and show empirically that the proposed approach satisfies imprecision bounds for more queries than the current state of the art. We also formulate the problem of predicate role mining for extraction of authorized selection predicates and propose an approximate algorithm. The access control for stream data allows roles access to tuples satisfying an authorized predicate sliding window query. The generalization introduces imprecision in the authorized view of stream. This imprecision can be reduced by delaying the publishing of stream data. However, the delay in sharing the stream tuples to access control can lead to false negatives. The challenge is to optimize the time duration for which the data is held by PPM so that the imprecision bound for maximum number of queries are met. We present the hardness results, provide an anonymization algorithm, and conduct experimental evaluation of the proposed algorithm.
590 $a School code: 0183.
650 4 $a Engineering, Computer. $3 1669061
650 4 $a Computer Science. $3 626642
650 4 $a Information Technology. $3 1030799
690 $a 0464
690 $a 0984
690 $a 0489
710 2 $a Purdue University. $b Electrical and Computer Engineering. $3 1018497
773 0 $t Dissertation Abstracts International $g 74-12B(E).
790 $a 0183
791 $a Ph.D.
792 $a 2013
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3592081