  Information diffusion in social networks facilitates rapid and large-scale
propagation of content. However, spontaneous diffusion behavior could also lead
to the cascading of sensitive information, which is neglected in prior arts. In
this paper, we present the first look into adaptive diffusion of sensitive
information, which we aim to prevent from widely spreading without incurring
much information loss. We undertake the investigation in networks with
partially known topology, meaning that some users' ability of forwarding
information is unknown. Formulating the problem into a bandit model, we propose
BLAG (Bandit on Large Action set Graph), which adaptively diffuses sensitive
information towards users with weak forwarding ability that is learnt from
tentative transmissions and corresponding feedbacks. BLAG enjoys a low
complexity of O(n), and is provably more efficient in the sense of half regret
bound compared with prior learning method. Experiments on synthetic and three
real datasets further demonstrate the superiority of BLAG in terms of adaptive
diffusion of sensitive information over several baselines, with at least 40
percent less information loss, at least 10 times of learning efficiency given
limited learning rounds and significantly postponed cascading of sensitive
information.
