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Privacy preserving data sharing.
~
Emekci, Fatih.
Privacy preserving data sharing.
紀錄類型:
書目-電子資源 : 單行本
正題名/作者:
Privacy preserving data sharing./
作者:
Emekci, Fatih.
面頁冊數:
181 p.
附註:
Source: Dissertation Abstracts International, Volume: 67-05, Section: B, page: 2652.
Contained By:
Dissertation Abstracts International67-05B.
標題:
Computer Science. -
電子資源:
Download PDF (下載PDF全文)
ISBN:
9780542682308
Privacy preserving data sharing.
Emekci, Fatih.
Privacy preserving data sharing.
- 181 p.
Source: Dissertation Abstracts International, Volume: 67-05, Section: B, page: 2652.
Thesis (Ph.D.)--University of California, Santa Barbara, 2006.
Recent economic trends are forcing enterprises to collaborate with each other to analyze the market in a better way and make intelligent decisions. Therefore, data integration from multiple autonomous data sources has emerged as an important practical problem. The key requirement is that owners of such data need to cooperate in a competitive landscape in most of the cases. The research challenge in developing a query processing solution is that the answers to the queries need to be provided while preserving the privacy of the data sources. In general allowing unrestricted read access to the whole data may give rise to potential vulnerabilities as well as may have legal implications. Therefore, there is a need for privacy preserving query processing methods for querying data residing at different parties. To satisfy these requirements we propose new query processing techniques to execute declarative queries spanning private data warehouses in a privacy preserving manner (i.e., only the query answer is revealed but nothing else). Along this direction, we explore different ways of building such a query processor with and without using third parties. Our scheme is able to answer queries without revealing any useful information to the data sources or to the third parties. Our approach uses lightweight computation and communication overhead thus making it scalable to large data sets. Furthermore, we looked at the decision tree learning over the union of multiple private data warehouses and propose scalable and efficient solutions to this problem. Finally, this thesis also proposes new data management tools for secure data outsourcing to protect the content of data from a database service provider. Overall, this work represents the initial steps in the expanding area of privacy preserving data management.
ISBN: 9780542682308Subjects--Topical Terms:
1000005419
Computer Science.
Privacy preserving data sharing.
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Recent economic trends are forcing enterprises to collaborate with each other to analyze the market in a better way and make intelligent decisions. Therefore, data integration from multiple autonomous data sources has emerged as an important practical problem. The key requirement is that owners of such data need to cooperate in a competitive landscape in most of the cases. The research challenge in developing a query processing solution is that the answers to the queries need to be provided while preserving the privacy of the data sources. In general allowing unrestricted read access to the whole data may give rise to potential vulnerabilities as well as may have legal implications. Therefore, there is a need for privacy preserving query processing methods for querying data residing at different parties. To satisfy these requirements we propose new query processing techniques to execute declarative queries spanning private data warehouses in a privacy preserving manner (i.e., only the query answer is revealed but nothing else). Along this direction, we explore different ways of building such a query processor with and without using third parties. Our scheme is able to answer queries without revealing any useful information to the data sources or to the third parties. Our approach uses lightweight computation and communication overhead thus making it scalable to large data sets. Furthermore, we looked at the decision tree learning over the union of multiple private data warehouses and propose scalable and efficient solutions to this problem. Finally, this thesis also proposes new data management tools for secure data outsourcing to protect the content of data from a database service provider. Overall, this work represents the initial steps in the expanding area of privacy preserving data management.
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Download PDF (下載PDF全文)
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