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Browse Prior Art Database

Intelligent Control of Browser Tracking

IP.com Disclosure Number: IPCOM000224901D
Original Publication Date: 2013-Jan-10
Included in the Prior Art Database: 2013-Jan-10
Document File: 3 page(s) / 38K

Publishing Venue

Microsoft

Related People

Gavin Smyth: INVENTOR [+4]

Abstract

Web sites have a very wide range of techniques available to them to track users’ behavior on the web, sometimes to the benefit of the user, sometimes not. End-user visibility, control and configuration of such facilities is minimal and mainly manual. Where features are configurable, the options are typically quite heavyweight can coarse grained (e.g., turn cookies completely on or off, on a per-domain basis The proposal here is to combine information from multiple data streams and apply machine learning techniques to automatically filter or modify these mechanisms to provide the user with more control over the exposure of his/her browsing behavior.

This text was extracted from a Microsoft Word document.
This is the abbreviated version, containing approximately 45% of the total text.

Document Author (alias)

Gavin Smyth (gavinsmy)

Defensive Publication Title 

Intelligent control of browser tracking

Name(s) of All Contributors

Gavin Smyth

Natasa Milic-Frayling

Jamie Costello

Gabriella Kazai

 

Summary of the Defensive Publication/Abstract

Web sites have a very wide range of techniques available to them to track users’ behavior on the web, sometimes to the benefit of the user, sometimes not. End-user visibility, control and configuration of such facilities is minimal and mainly manual. Where features are configurable, the options are typically quite heavyweight can coarse grained (e.g., turn cookies completely on or off, on a per-domain basis

The proposal here is to combine information from multiple data streams and apply machine learning techniques to automatically filter or modify these mechanisms to provide the user with more control over the exposure of his/her browsing behavior.

Description:  Include architectural diagrams and system level data flow diagrams if: 1) they have already been prepared or 2) they are needed to enable another developer to implement your defensive publication. Target 1-2 pages, and not more than 5 pages.  

The most visible and well known mechanism for monitoring users is to use cookies but this is merely one aspect of an arsenal of tools web sites have for “tracking” our behavior – other items are directly related to network traffic of some sort or served up content (“web bugs” such as invisible images, or even image pixel values themselves), as well as data stored in various forms of local storage (Flash, Silverlight, HTML5). Note that many of these techniques can be applied to email clients as well as to web browsing.

Most of these tracking systems came into existence to provide some benefit to the user (such as remembering preferences or current user state across sessions) but there are privacy concerns (in fact, the cookie standard specifications, RFC 2109 & 2965, specify that browsers should protect users’ privacy) and all these mechanisms have been subverted to permit web sites to track users’ behavior, often to target advertisements. The fact that user behavior is tracked and stored can be felt to compromise user security. One risk here is third parties “joining the dots” across multiple sites – either directly via shared items on a web page, or indirectly by merging multiple databases and using heuristics to match different accounts to the same user – which can provide a detailed picture of users, which could expose them to unexpected invasions of privacy.

http://www.grc.com/cookies/operation.htm provides a good overview of how HTTP cookies work, and the risks to one’s anonymity. http://samy.pl/evercookie/ offers an “ever cookie” which is very difficult to completely delete, using a number of different browser technologies (so-called Flash cookies, local storage, etc.) – “zombie cookies” (http://en.wikipedia.org/wiki/Zombie_cookie) are similar. Things...