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A framework for privacy-enhanced per...

Wang, Yang.

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A framework for privacy-enhanced personalization.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	A framework for privacy-enhanced personalization./
作者:	Wang, Yang.
面頁冊數:	222 p.
附註:	Source: Dissertation Abstracts International, Volume: 71-04, Section: B, page: 2511.
Contained By:	Dissertation Abstracts International71-04B.
標題:	Computer Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3397191
ISBN:	9781109686678

A framework for privacy-enhanced personalization.
Wang, Yang.

A framework for privacy-enhanced personalization. - 222 p.

Source: Dissertation Abstracts International, Volume: 71-04, Section: B, page: 2511.

Thesis (Ph.D.)--University of California, Irvine, 2010.

Web personalization has demonstrated to be advantageous for both online customers and vendors. However, its benefits may be severely counteracted by privacy constraints. Personalized systems need to address these privacy constraints, including users' personal privacy preferences as well as privacy laws and industry self-regulations that may be in effect.

ISBN: 9781109686678Subjects--Topical Terms:

626642
Computer Science.

A framework for privacy-enhanced personalization.
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500 $a Source: Dissertation Abstracts International, Volume: 71-04, Section: B, page: 2511.
500 $a Adviser: Alfred Kobsa.
502 $a Thesis (Ph.D.)--University of California, Irvine, 2010.
520 $a Web personalization has demonstrated to be advantageous for both online customers and vendors. However, its benefits may be severely counteracted by privacy constraints. Personalized systems need to address these privacy constraints, including users' personal privacy preferences as well as privacy laws and industry self-regulations that may be in effect.
520 $a This research aims to reconcile privacy and web personalization. In particular, this research proposes user-tailored privacy enforcement in personalization, i.e., catering privacy to the situation of each individual user. This research develops a framework for privacy-enhanced personalization based on product line architecture (PLA). This framework provides a systematic and flexible way to model, manage and enforce applicable privacy constraints in web personalization. Our systematic analysis of over 40 privacy laws shows that these legal requirements can not only affect the collection, storage and transfer of personal data, but also the methods that may be used to process the data in personalized systems. This framework dynamically selects and de-selects personalization methods during runtime in observance of each user's applicable privacy constraints.
520 $a A controlled experiment shows that users value the user-tailored privacy enforcement in personalization: they have higher regard for the privacy practices of a personalized system, disclose more information about themselves to the system, and do not perceive less pesonalization benefit. Besides, a performance evaluation of our framework shows that it is technically feasible for internationally-operating personalized sites even with the highest traffic today.
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790 1 0 $a van der Hoek, Andre $e committee member
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790 1 0 $a Redmiles, David $e committee member
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