Social Internet of Things (SIoT) integrates social network schemes into Internet of Things (IoT), which provides opportunities for\nIoT objects to form social communities. Existing social network models have been adopted by SIoT paradigm. The wide distribution\nof IoT objects and openness of social networks, however, make it more challenging to preserve privacy of IoT users. In\nthis paper, we present a novel framework that preserves privacy against inference attacks on social network data through ranked\nretrieval models. We propose PVS, a privacy-preserving framework that involves the design of polymorphic value sets and ranking\nfunctions. PVS enables polymorphism of private attributes by allowing them to respond to different queries in different ways. We\nbegin this work by identifying two classes of adversaries, authenticity-ignorant adversary, and authenticity-knowledgeable\nadversary, based on their knowledge of the distribution of private attributes. Next, we define the measurement functions of utility\nloss and propose PVSV and PVST that preserve privacy against authenticity-ignorant and authenticity-knowledgeable adversaries,\nrespectively. We take into account the utility loss of query results in the design of PVSV and PVST. Finally, we show that\nPVSV and PVST meet the privacy guarantee with acceptable utility loss in extensive experiments over real-world datasets.
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