  Cell zooming has been becoming an essential enabler for off-grid small cell
networks. Traditional models often utilize the numbers of active users in order
to determine cell zooming strategies. However, such confidential measurement
data must be concealed from others. We therefore propose a novel cell zooming
method with masking noise. The proposed algorithm is designed based on
distributed optimization, in which each SBS locally solves a divided
optimization problem and learns how much a global constraint is satisfied or
violated for temporal solutions. The important feature of this distributed
control method is robustness against masking noise. We analyze the trade-off
between confidentiality and optimization accuracy, using the notion of
differential privacy. Numerical simulations show that the proposed distributed
control method outperforms a standard centralized control method in the
presence of masking noise.
