User tracking without cookies
Could be a real money spinner and/or invaluable way to track your vids ending up on the tubes (combine with quantum-x's open source method of adding frames to vids to track vids).
Basically a fantastic way to track the vast majority of your users without using cookies, but simply their unique browser fingerprint....
the science
test yourself
I've been "tracking" my JS users for 7 days (not 'my' users but those of sites that use my service) and I have to say it looks pretty remarkable to date with 9.2 million unique fingerprints and 38 million "tracked" fingerprints over that time period (8 million had to be 'tracked' using the python script to match signatures that had gone through updates (browser update or font addition/deletion) ). Disclaimer though, as I don't 'own' those users and so set cookies etc I can't say at all what my false positive/negative rate is.
This could be *very* big indeed and could easily find its place in DRMing vids to find out who uploads stolen content (unless CDNs are used to distribute said content)... tag in an md5 hash of the browser fingerprint and check that off against member logins/hash's.
I remember wanting a browser uniqueness possibility about 2 yrs ago but everything I tried was too unreliable - this however, with its bit scoring to give confidence values on uniqueness is a frikken gem
If you can understand the reasoning and algorithms behind it. knock yourselves out! I have millions of hits but it's not my traffic, so I can't experiment with understanding if I'm dealing with false positives (tracking one unique user that is in fact multiple different people) or false negatives (scoring multiple uniques when in fact it's the same person). If you have something similar in traffic, then give it a go and compare with unique cookie IDs over a period to see if you've got a good method of server-side tracking...
Basically a fantastic way to track the vast majority of your users without using cookies, but simply their unique browser fingerprint....
the science
test yourself
I've been "tracking" my JS users for 7 days (not 'my' users but those of sites that use my service) and I have to say it looks pretty remarkable to date with 9.2 million unique fingerprints and 38 million "tracked" fingerprints over that time period (8 million had to be 'tracked' using the python script to match signatures that had gone through updates (browser update or font addition/deletion) ). Disclaimer though, as I don't 'own' those users and so set cookies etc I can't say at all what my false positive/negative rate is.
This could be *very* big indeed and could easily find its place in DRMing vids to find out who uploads stolen content (unless CDNs are used to distribute said content)... tag in an md5 hash of the browser fingerprint and check that off against member logins/hash's.
I remember wanting a browser uniqueness possibility about 2 yrs ago but everything I tried was too unreliable - this however, with its bit scoring to give confidence values on uniqueness is a frikken gem
If you can understand the reasoning and algorithms behind it. knock yourselves out! I have millions of hits but it's not my traffic, so I can't experiment with understanding if I'm dealing with false positives (tracking one unique user that is in fact multiple different people) or false negatives (scoring multiple uniques when in fact it's the same person). If you have something similar in traffic, then give it a go and compare with unique cookie IDs over a period to see if you've got a good method of server-side tracking...
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