Method, a device and computer program products for protecting privacy of users from web-trackers

1. A method for protecting privacy of users from web-trackers, the method comprising:

capturing, by a device ( 200 ), a public unique identifier set by a Website ( 300 ) in a computing device ( 100 D) of a user ( 100 ) who requested access to said Website ( 300 ) via the Internet,

removing, by the device ( 200 ), the captured public unique identifier from said users' computing device ( 100 D);

monitoring, by the device ( 200 ), during a first configurable period of time, web-requests the user ( 100 ) makes to said Website ( 300 ) and/or to other different Websites to obtain a web-behavioral profile of the user ( 300 ), and storing the obtained web-behavioral profile as a first vector representing what information of the user ( 100 ) has been allowed to pass to a web-tracker;

upon expiration of the first configurable period of time, tracking, by the device ( 200 ), during a second configurable period of time, the web-requests made by the user ( 100 ), to examine the effect each web-request has on assisting the de-anonymization of the user ( 100 ), obtaining a second vector;

classifying, by the device ( 200 ), the obtained second vector taking into account a computed similarity score parameter that considers the web-behavioral profile of the user ( 100 ) with respect to a web-behavioral profile of one or more users and a threshold parameter related with the privacy of the user ( 100 );

creating and mapping, by the device ( 200 ), a corresponding private unique identifier for said captured public identifier, said created and mapped private unique identifier being based on said classification; and

executing, by the device ( 200 ), based on said mapping between the private and the public unique identifiers, an intervention algorithm for said web-tracker to protect privacy of the user ( 100 ) therein taking into account a configured intervention policy.

2. The method of claim 1 , wherein said configured intervention policy comprises refusing to return said public unique identifier to the web-tracker.

3. The method of claim 1 , wherein said configured intervention policy comprises, when the user ( 100 ) is determined to be classified below said threshold parameter, searching through the second vector of another user for a web-request that when added to the user's second vector minimizes the following function:

Δ(θ(sig, u ′),θ(sig, u ))−Δ(θ( u′,v ),θ( u,v ))

where u′ is the user's history with the addition of a candidate web-request from said another user, or v's, history and Δ is the difference between two similarity scores.

4. The method of claim 1 , wherein said configured intervention policy comprises using a pseudonym associated with the user ( 100 ) by mapping the real name of the user ( 100 ) with the created pseudonym.

5. The method of claim 1 , wherein said configured intervention policy comprises returning to the web-tracker a public unique identifier of another user.

6. The method of claim 5 , wherein said another user have a similar web-behavioral profile to the user ( 100 ).

7. The method of claim 1 , wherein said classifying step comprises ranking the obtained second vector.

8. The method of claim 1 , wherein said capturing step being performed the first time the user ( 100 ) requests access to said Website ( 300 ).

9. The method of claim 1 , comprising monitoring, during the first configurable period of time, all the web-requests the user ( 100 ) makes to the Website ( 300 ).

10. The method of claim 1 , wherein said public and/or private unique identifier comprises at least one of cookies, IP addresses or browsers fingerprints.

11. The method of claim 1 , wherein said device ( 200 ) comprises a middlebox or a web proxy server.

12. A device for protecting privacy of users from web-trackers, comprising:

a processor; and

a memory having a plurality of computer program code instructions embodied thereon and configured to be executed by the processor, the plurality of computer program code instructions comprising instructions to:

capture a public unique identifier set by a Website ( 300 ) in a computing device ( 100 D) of a user ( 100 ) who visit said Website ( 300 ) via the Internet,

remove, the captured public unique identifier from said users' computing device ( 100 D);

monitor, during a first configurable period of time, web-requests the user ( 100 ) makes to said Website ( 300 ) and/or to other different Websites to obtain a web-behavioral profile of the user ( 300 ), and storing the obtained web-behavioral profile as a first vector representing what information of the user ( 100 ) has been allowed to pass to a web-tracker;

upon expiration of the first configurable period of time, track, during a second configurable period of time, the web-requests made by the user ( 100 ), to examine the effect each request has on assisting the de-anonymization of the user ( 100 ) obtaining a second vector;

classify, the obtained second vector taking into account a computed similarity score parameter that considers the web-behavioral profile of the user with respect to a web-behavioral profile of one or more users and a threshold parameter related with the privacy of the user ( 100 );

create and map, a corresponding private unique identifier for said captured public identifier, said created and mapped private unique identifier being based on said classification; and

execute, based on said mapping between the private and the public unique identifiers, an intervention algorithm for said web-tracker to protect privacy of the user ( 100 ) therein taking into account a configured intervention policy.

13. The device of claim 12 , being a middlebox.

14. The device of claim 12 , being a web proxy server including at least CDN nodes or web caches, acceleration proxies for wired or wireless networks, or VPN proxies.

15. The device of claim 12 , wherein said public and/or private unique identifier comprises at least one of cookies, IP addresses or browsers fingerprints.

16. A non-transitory computer readable medium comprising program code instructions which when loaded into a computer system controls the computer system to protect privacy of users from web-trackers by:

capturing a public unique identifier set by a Website ( 300 ) in a computing device ( 100 D) of a user ( 100 ) who requested access to said Website ( 300 ) via the Internet;

removing the captured public unique identifier from said users' computing device ( 100 D);

monitoring, during a first configurable period of time, web-requests the user ( 100 ) makes to said Website ( 300 ) and/or to other different Websites to obtain a web-behavioral profile of the user ( 300 ), and storing the obtained web-behavioral profile as a first vector representing what information of the user ( 100 ) has been allowed to pass to a web-tracker;

upon expiration of the first configurable period of time, tracking, during a second configurable period of time, the web-requests made by the user ( 100 ), to examine the effect each web-request has on assisting the de-anonymization of the user ( 100 ), obtaining a second vector;

classifying the obtained second vector taking into account a computed similarity score parameter that considers the web-behavioral profile of the user ( 100 ) with respect to a web-behavioral profile of one or more users and a threshold parameter related with the privacy of the user ( 100 );

creating and mapping a corresponding private unique identifier for said captured public identifier, said created and mapped private unique identifier being based on said classification; and

executing, based on said mapping between the private and the public unique identifiers, an intervention algorithm for said web-tracker to protect privacy of the user ( 100 ) therein taking into account a configured intervention policy.

17. The method of claim 1 , wherein the computed similarity score parameter identifies a similarity between a current web history of the user ( 100 ) and the web-behavioral profile of the user ( 100 ), and another set of computed similarity score parameters identifies similarities between web histories of the one or more users and the web-behavioral profile of the user ( 100 ); and

where classifying the obtained second vector further comprises:

classifying the obtained second vector based on the computed similarity score parameter and the other set of computed similarity score parameters.

18. The method of claim 1 , wherein the threshold parameter identifies a number of other users, of the one or more users, that include a respective web-behavioral profile that is more similar to the web-behavioral profile of the user ( 100 ) than a current web history of the user ( 100 ) is to the web-behavioral profile of the user ( 100 ).

19. The method of claim 1 , further comprising:

reducing a rank of the user ( 100 ) below another rank identified by the threshold parameter; and

where executing the intervention algorithm further comprises:

executing the intervention algorithm based on reducing the rank of the user ( 100 ) below the other rank identified by the threshold parameter.

20. The method of claim 1 , further comprising:

determining other computed similarity score parameters, of the one or more users, based on web-behavioral profiles of the one or more users and the web-behavioral profile of the user ( 100 ); and

where classifying the obtained second vector further comprises:

classifying the obtained second vector based on the other computed similarity score parameters.