Information Technology Reference
In-Depth Information
A user can also learn its social tie level with another user without disclosing one's
real identity to the other. To this end, each user keeps a social profile consisting of
the social communities that it belongs to (e.g., a community of colleagues at the
same workplace), and sets a
single
social tie level for each community based on its
social relationships with those in the community. Each community is identified by
a predefined key that is only known to the community's members. Using a certain
private matching protocol such as [
14
,
15
], two users can learn whether they have
a community in common, and if yes, which community
5
it is. In particular, the
protocol involves several message exchanges between the two users, including one
message that contains encrypted values that are functions of the keys of a user's social
communities. The protocol ensures that both users can only know the community
they have in common (if it exists), and neither user can learn any additional social
information of the other, or pretend to have a community in common with the other.
Since a community typically has many members, neither user can know the other's
real identity even when they know the community they both belong to. In addition,
since social information is encrypted in the messages, the platform cannot learn any
user's social information. Therefore, each user can learn its social tie levels with
those in its potential anonymity set while keeping their real identities private. Note
that although the adversary might collude with multiple users, it is almost infeasible
for the adversary to find a significant number of colluding users who have social ties
with a specific user, in order to infer the user's real identity.
5.6
Numerical Results
In this section, we provide numerical results to illustrate the system efficiency of the
Pareto-optimal SNE computed by Algorithm 2. We compare the social welfare of
the Pareto-optimal SNE with the maximum social welfare of all SNEs for SO-PCG
and the optimal social welfare, which is found by exhaustive search.
We consider
N
mobile users who are interested in participating in pseudonym
change. They are randomly located in a square area with side length 500 m. We
assume that the anonymity range of each user is a disk centered at the user's location
with radius randomly chosen from
. Based on users' physical
locations and anonymity ranges, there exists a physical edge from user
i
to user
j
if
user
i
is in the anonymity range of user
j
.
We
assume that a user's cost of changing
pseudonym is uniformly distributed in [0,
C
]. We simulate the social graph using
two methods as follows.
{
200 m, 300 m, 400 m
}
5
To protect privacy, only one community in common is revealed if they have multiple communities
in common.
