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Information diffusion in social networks facilitates rapid and large-scalepropagation of content. However, spontaneous diffusion behavior could also leadto the cascading of sensitive information, which is neglected in prior arts. Inthis paper, we present the first look into adaptive diffusion of sensitiveinformation, which we aim to prevent from widely spreading without incurringmuch information loss. We undertake the investigation in networks withpartially known topology, meaning that some users' ability of forwardinginformation is unknown. Formulating the problem into a bandit model, we proposeBLAG (Bandit on Large Action set Graph), which adaptively diffuses sensitiveinformation towards users with weak forwarding ability that is learnt fromtentative transmissions and corresponding feedbacks. BLAG enjoys a lowcomplexity of O(n), and is provably more efficient in the sense of half regretbound compared with prior learning method. Experiments on synthetic and threereal datasets further demonstrate the superiority of BLAG in terms of adaptivediffusion of sensitive information over several baselines, with at least 40percent less information loss, at least 10 times of learning efficiency givenlimited learning rounds and significantly postponed cascading of sensitiveinformation.

BLAG: Bandit On Large Action Set Graph