روشهای محافظت از حریم خصوصی وب
ترجمه نشده

روشهای محافظت از حریم خصوصی وب

عنوان فارسی مقاله: مقایسه روشهای محافظت از حریم خصوصی وب
عنوان انگلیسی مقاله: A comparison of web privacy protection techniques
مجله/کنفرانس: ارتباطات رایانه ای - Computer Communications
رشته های تحصیلی مرتبط: کامپیوتر، فناوری اطلاعات
گرایش های تحصیلی مرتبط: امنیت اطلاعات، اینترنت و شبکه های گسترده، مهندسی نرم افزار، سامانه های شبکه ای
کلمات کلیدی فارسی: محافظت از حریم خصوصی وب، اندازه گیری
کلمات کلیدی انگلیسی: Web privacy protection، Measurements
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus - Master Journals List - JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.comcom.2019.04.005
دانشگاه: National Institute of Informatics, JFLI, ANSSI, Japan
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2019
ایمپکت فاکتور: 3/727 در سال 2019
شاخص H_index: 91 در سال 2020
شاخص SJR: 0/500 در سال 2019
شناسه ISSN: 0140-3664
شاخص Quartile (چارک): Q2 در سال 2019
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 13
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E13303
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست انگلیسی مطالب

Abstract


1- Introduction


2- Background


3- Related work


4- Methodology


5- Measurement parameters


6- Results


7- Discussions


8- Conclusions


References

نمونه متن انگلیسی مقاله

Abstract


Tracking is pervasive on the web. Third party trackers acquire user data through information leak from websites, and user browsing history using cookies and device fingerprinting. In response, several privacy protection techniques (e.g. the Ghostery browser extension) have been developed. To the best of our knowledge, our work is the first study that proposes a reliable methodology for privacy protection comparison, and extensively compares a wide set of privacy protection techniques. Our contributions are the following. First, we propose a robust methodology to compare privacy protection techniques when crawling many websites, and quantify measurement error. To this end, we reuse the privacy footprint and apply the Kolmogorov–Smirnov test on browsing metrics. This test is likewise applied to HTML-based metrics to assess webpage quality degradation. To complement HTML-based metrics, we also design a manual analysis. Second, we study the overlap of blocking resources between most popular browser extensions, and compare the performances using the proposed methodology. We show that protection techniques have vastly different performances, and that the best of them exhibit a wide overlap. Next, we analyze the impact of privacy protection techniques on webpage quality. We show that automated HTML-based analysis sometimes fails to expose quality reduction perceived by users. Finally, we provide a set of usage recommendations for end-users and research recommendations for the scientific community. Ghostery and uBlock Origin provide the best trade-off between protection and webpage quality. Ghostery however requires a configuration step which is difficult for users. The RequestPolicy Continued and NoScript extensions exhibit the best performances but reduce webpage quality. Ghostery and uBlock Origin use manually built blocking lists which are cumbersome to maintain. Research efforts should focus on improving existing approaches that do not rely on blocking lists (such as Privacy badger or MyTrackingChoices), and automatically building reliable blocking lists.


Introduction


The huge growth of the Internet comes along with an everincreasing advertising market. Internet users access content provided for free by publishers. Consequently, publishers monetize their audience through advertisement. Companies thus buy online exposure to promote their products. In order to maximize advertisement efficiency, advertisers tailor ads to users regarding their interests. To this end, advertisers leverage context (e.g. visited website) or previous browsing interests. Advertisers use techniques such as cookies to identify users across websites and build their browsing history. Other techniques have also been developed to allow advertising actors to communicate with each other (such as cookie syncing [1]), or circumvent cookie removal by respawning cookies using diverse types of data storage inside the browser (e.g. using Flash [2]). Browser fingerprinting [3] allows a tracking entity to follow a user across websites without any in-browser data storage. In response to these techniques, several counter-measures were designed. We can here quote the Do Not Track HTTP header [4] by which a user can ask not to be tracked. Browsers can also block some or all cookies. Finally, many browser extensions hinder third party tracking by preventing cookie creation and/or blocking requests to tracking services.

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