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Fig. 5.9 Average number of
social edges per user versus
N for the real data trace
0.5
0.45
0.4
0.35
0.3
0.25
0.2
12
13
14
15
16
17
18
19
20
Number of Users
Fig. 5.10 Impact of N for the
real data trace
1.4
Social−Oblivious
Social−Aware
Social Optimal
1.3
1.2
1.1
1
0.9
12
13
14
15
16
17
18
19
20
Number of Users
see that the performance gain of socially-aware users can achieve up to 15 % over
socially-aware users, while its performance gap from the optimal social welfare is
less than 10 % on average. This verifies the effectiveness of exploiting social ties for
improving location privacy based on real social data.
5.7
Summary
In this chapter, we study the SGUM-based pseudonym change for personalized
location privacy. The SGUM-based PCG is based on a general anonymity model that
allows each user to have its specific anonymity set. For the SGUM-based PCG, we
show that there exists a SNE. Then we develop an algorithm that greedily determines
users' strategies, based on the social group utility derived from only the users whose
strategies have been determined. We show that this algorithm can efficiently find
a Pareto-optimal SNE with social welfare higher than that corresponding to the
socially-oblivious PCG. Numerical results demonstrate that social welfare can be
significantly improved by exploiting users' social ties.
 
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