  Cloud service providers typically provide different types of virtual machines
(VMs) to cloud users with various requirements. Thanks to its effectiveness and
fairness, auction has been widely applied in this heterogeneous resource
allocation. Recently, several strategy-proof combinatorial cloud auction
mechanisms have been proposed. However, they fail to protect the bid privacy of
users from being inferred from the auction results. In this paper, we design a
differentially private combinatorial cloud auction mechanism (DPCA) to address
this privacy issue. Technically, we employ the exponential mechanism to compute
a clearing unit price vector with a probability proportional to the
corresponding revenue. We further improve the mechanism to reduce the running
time while maintaining high revenues, by computing a single clearing unit
price, or a subgroup of clearing unit prices at a time, resulting in the
improved mechanisms DPCA-S and its generalized version DPCA-M, respectively. We
theoretically prove that our mechanisms can guarantee differential privacy,
approximate truthfulness and high revenue. Extensive experimental results
demonstrate that DPCA can generate near-optimal revenues at the price of
relatively high time complexity, while the improved mechanisms achieve a
tunable trade-off between auction revenue and running time.
