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Preference sharing-A Study On Anonymity

Preference sharing-A Study On Anonymity. Team Neighbors. Progress Report II. Why Full Bruteforce Failed?. Generating k-plexes (Limited). We first find the cliques in a network, for a given size

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Preference sharing-A Study On Anonymity

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  1. Preference sharing-A Study On Anonymity Team Neighbors Progress Report II

  2. Why Full Bruteforce Failed?

  3. Generating k-plexes (Limited) • We first find the cliques in a network, for a given size • Then for each node, we take one clique at a time and check if that is a part connected to atleast s-k other nodes. • If we were able to find 10 k-plexes this way, then we stop.if not then we reduce the search to find a kplex size of one lesser than the previous and check for the k-plexes. • Similarly we decrement the size if less than 10 kplexes are found.

  4. Graph of NetScienceKplexes • For total nodes: 1588 & k=3.

  5. Propagation model • Load synNw • Load kplexes • For all users, we find all the k-plexes to which it belongs, this forms the uanons. • Then for all Uanons, we check if each users in synnw is connected to atleast t users in Uanon. If yes then it becomes a part of the extended set. • We then set preferences by choosing randomly a resource and a preference for it. • A pair of a random uanon of a user, its pref and resource is set which is then fed to the adversarial model.

  6. Adversarial Model • Input file 1 format: Uanon Resource Preference • Picks up Rare Resource-Preference pair • Stores the corresponding Uanon numbers in a List.

  7. Input file 2 format: Uanon Member1 Member2… Member(n) • Traverse this file and store the member list of the Uanons found in the previous step. • Perform step intersection of 2 member lists at a time and perform the next intersection with the next member list and the current intersection result.

  8. KplexNeighbors KplexSmart Preferences -Node -resource -preference Node -Uanons Resources -resource Adversarial Model

  9. Success evaluation • If the final intersection list contains 0 member then success =0% • If the final intersection list contains 1 member then success =100% • If the final intersection list contains 2 member then success =50% • And so on…

  10. This week • Hit the propagation model with the adversaral model • Perform same experiment with different k values. • Perform everything with other datasets.

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