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Web Filtering and Censoring This paper appears in: Computer Issue Date : March 2010 Volume : 43, Issue:3 On page(s): 94 ISSN : 0018-9162 INSPEC Accession.

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Presentation on theme: "Web Filtering and Censoring This paper appears in: Computer Issue Date : March 2010 Volume : 43, Issue:3 On page(s): 94 ISSN : 0018-9162 INSPEC Accession."— Presentation transcript:

1 Web Filtering and Censoring This paper appears in: Computer Issue Date : March 2010 Volume : 43, Issue:3 On page(s): 94 ISSN : 0018-9162 INSPEC Accession Number: 11171565 Digital Object Identifier : 10.1109/MC.2010.84 Date of Current Version : 11 三月 2010 Sponsored by : IEEE Computer SocietyComputerIssue:310.1109/MC.2010.84IEEE Computer Society Speaker: 侯思帆

2 摘要 前言 ▫Green Dam Youth Escort Web filter How web filter works ▫IP and URL blackist ▫Content filtering Green Dam

3 IP and URL blackist 缺點 : ▫First.continual blackist updating requires enormous effort and resources. ▫Second IP blacklist work at a coarse granularity-the filter either blocks or allows all web content from an IP address.

4 IP and URL blackist Implemented at DNS servers that resolve domain names to ip address. When a DNS server receives a recolution request, it check the URL is on the list, the DNS server will return an incorrect or default IP address.

5 IP and URL blackist

6

7 Content filtering Advantages Over IP or URL blacklist First,ther filter examines web contents when a page is requested,without the need to pre- establish a blacklist. Second the filtering decision is based on individual web-pages or even elements within a page.

8 IP and URL blackist

9 Content filtering Examine various elements of a webpage for classification, including the metadata,link,text,images and scripts

10 Content filtering(Metadata)

11 Content filtering Link ▫For example, a new website is likely linked to other the news sites. Text ▫Semantic and keyword

12 Content filtering Image ▫Skin, but color different

13 Green Dam First ▫check URL blacklist Second ▫cans text obscenities and politically sensitive phrases Third ▫Image scanner (skin color) ▫Svm(support vector machine), 以其它過濾條件補 強

14 Green Dam(filtering content) Political dissent( 持不同政治意見 ) Social deviance for example pornography,gambling National security threats- terrorism Certain internet services – blogs,peer to peer

15 China(black list)

16 Filter Circumvention 請多使用 green dam


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