Benchmarking the Privacy-Preserving People Search
Shuguang Han, Daqing He, Zhen Yue

TL;DR
This study evaluates how privacy concerns in social networks affect the effectiveness of people search algorithms, highlighting the differential impacts of privacy on local and global network features.
Contribution
It introduces a simulation approach to analyze privacy effects on people search performance using publicly available social network data.
Findings
Privacy concerns vary among individuals based on their network degree.
Local network features are more sensitive to privacy restrictions than global features.
High-degree individuals' privacy significantly impacts search performance.
Abstract
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…
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Taxonomy
TopicsMobile Crowdsensing and Crowdsourcing · Human Mobility and Location-Based Analysis · Complex Network Analysis Techniques
