People search is an important topic in information retrieval. Many previous
studies on this topic employed social networks to boost search performance by
incorporating either local network features (e.g. the common connections
between the querying user and candidates in social networks), or global network
features (e.g. the PageRank), or both. However, the available social network
information can be restricted because of the privacy settings of involved
users, which in turn would affect the performance of people search. Therefore,
in this paper, we focus on the privacy issues in people search. We propose
simulating different privacy settings with a public social network due to the
unavailability of privacy-concerned networks. Our study examines the influences
of privacy concerns on the local and global network features, and their impacts
on the performance of people search. Our results show that: 1) the privacy
concerns of different people in the networks have different influences. People
with higher association (i.e. higher degree in a network) have much greater
impacts on the performance of people search; 2) local network features are more
sensitive to the privacy concerns, especially when such concerns come from high
association peoples in the network who are also related to the querying user.
As the first study on this topic, we hope to generate further discussions on
these issues.Comment: 4 pages, 5 figure