You can be identified by your clickprint

[Kristijntje]

Is it possible to be identified by your 'clickprint'? 

Almost certainly - and if you're wondering what a clickprint is, it is "a unique pattern of web surfing behavior based on actions such as the number of pages viewed per session, the number of minutes spent on each page, the time or day of the week the page is visited, and so on." That's the description used by Professor Balaji Padmanabhan, at the Wharton School at the University of Pennsylvania, and Professor Catherine Yang, of the Graduate School of Management at the University of California, Davis.

In a new paper (available from http://tinyurl.com/hj263), they suggest that by observing how people navigate around a site over a number of sessions, an e-commerce company could distinguish between two anonymous surfers. That could have important implications in preventing fraud: if someone signed in with an existing user's logon, but their clickprint differed, that might be an indication that their ID had been stolen...

full article: http://technology.guardian.co.uk/weekly/...14,00.html

Clickprints on the Web: Are there signatures in Web browsing data?

We address the question of whether humans have unique signatures - or clickprints - when they browse the Web. The importance of being able to answer this can be significant given applications to electronic commerce in general and in particular online fraud detection, a major problem in electronic commerce posting the economy billions of dollars annually. In this paper we present a data mining approach to answer this “unique clickprint determination problem”. The solution technique is based on a simple yet powerful idea of casting this problem as an aggregation problem. We develop formal methods to solve this problem and thereby determine the optimal amount of user data that must be aggregated before unique clickprints can be deemed to exist. Using some basic behavioral variables derived from real Web browsing data, our results suggest that the answer to the main question is “most likely”, given that we
observe reasonably accurate user predictions using limited data...

full research paper: http://knowledge.wharton.upenn.edu/paper...ID=2045439&CFTOKEN=76928842