Privacy-Preserving Relationship Path Discovery in Social Networks

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Secure Federated Submodel Learning (2019, background): “…(PSC) [78]– [80], which have received tremendous attention and also have seen several practical applications, such as in social networks [81], [82]; human genome testing [83]; location-based services [84]; security incident sharing [85]; online advertising [85]; private contact…”
Faster Private Set Intersection Based on OT Extension (2014, methodology): “PSI is used for privacy-preserving computation of functionalities such as relationship path discovery in social networks [41], botnet detection [45], testing of fully-sequenced human genomes [3], proximity testing [48], or cheater detection in online games [11].”
Strathprints Institutional Repository (unknown): “For example, PSI has been proposed as a building block in applications such as privacy preserving data mining [4], human genome research [6], homeland security [15], Botnet detection [34], social networks [33], location sharing [36] and cheater detection in online games [11].”
Scalable Private Set Intersection Based on OT Extension (2018, methodology): “…was used in several research projects for privacy-preserving computation of functionalities such as relationship path discovery in social networks [Mezzour et al. 2009], botnet detection [Nagaraja et al. 2010], testing of fully-sequenced human genomes [Baldi et al. 2011], proximity testing…”