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Identifying private data leakage thr...

Zhao, Bin.

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Identifying private data leakage threats in web browsers.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Identifying private data leakage threats in web browsers./
作者:	Zhao, Bin.
面頁冊數:	135 p.
附註:	Source: Dissertation Abstracts International, Volume: 77-03(E), Section: A.
Contained By:	Dissertation Abstracts International77-03A(E).
標題:	Information science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3730712
ISBN:	9781339170008

Identifying private data leakage threats in web browsers.
Zhao, Bin.

Identifying private data leakage threats in web browsers. - 135 p.

Source: Dissertation Abstracts International, Volume: 77-03(E), Section: A.

Thesis (Ph.D.)--The Pennsylvania State University, 2015.

Modern web browsers now provide more customizations to improve the usability and their competitiveness. Browser extensions and private browsing mode (PBM) are arguably two most popular customizations. With billions of downloads, browser extensions enhance user experience by providing additional features. PBM enables users to browse the Internet while protecting their private browsing data. However, private data leakage threats still exist in browser extensions, even if under PBM.

ISBN: 9781339170008Subjects--Topical Terms:

554358
Information science.

Identifying private data leakage threats in web browsers.
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500 $a Source: Dissertation Abstracts International, Volume: 77-03(E), Section: A.
500 $a Adviser: Peng Liu.
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520 $a Modern web browsers now provide more customizations to improve the usability and their competitiveness. Browser extensions and private browsing mode (PBM) are arguably two most popular customizations. With billions of downloads, browser extensions enhance user experience by providing additional features. PBM enables users to browse the Internet while protecting their private browsing data. However, private data leakage threats still exist in browser extensions, even if under PBM.
520 $a In this dissertation, we first investigate two aspects of private data leakage threats associated with browser extensions: (1), aspect-level behavior clustering on browser extensions and its security implications, and (2), identifying privacy breaches caused by extensions under PBM.
520 $a First, many extensions can be downloaded from webstores without sufficient trust or safety scrutiny, which poses threats on user's private data. In this dissertation, we propose an aspect-level behavior clustering approach to enhancing the safety management of extensions. We decompose an extension's runtime behavior into several pieces, denoted as AEBs (Aspects of Extension Behavior). Similar AEBs of different extensions are grouped into an "AEB cluster" based on subgraph isomorphism. We then build profiles of AEB clusters for both extensions and categories (of extensions) to detect suspicious extensions.
520 $a Second, browser extensions can greatly undermine PBM, mostly due to the fact that browsers let extensions handle the private data themselves even if under PBM. We propose an approach to comprehensively identify and stop privacy breaches caused by browser extensions under PBM. We combine dynamic analysis and symbolic execution to represent extensions' behavior. Our analysis shows that many extensions have not fulfilled PBM's guidelines on handling private browsing data. The evaluation results on 1,912 Firefox extensions show that our approach can effectively identify and stop privacy breaches under PBM caused by extensions, with almost negligible performance impact.
520 $a Finally, we extend system-level behavior analysis on Android platform. We intend to map system level behavior with Android APIs, for further study to detect possible permission abusing.
590 $a School code: 0176.
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