It is expected that a large number of different objects, such as sensor devices and consumer electronics, will be connected to future networks. For such networks, we propose a name resolution method for directly specifying a condition on a set of attribute-value pairs of real-world information without needing prior knowledge of the uniquely assigned name of a target object, e.g., a URL. For name resolution, we need an algorithm to find the target object(s) satisfying a query condition on multiple attributes. To address the problem that multi-attribute searching algorithms may not work well when the number of attributes (i.e., dimensions) d increases, which is related to the curse of dimensionality, we also propose a probabilistic searching algorithm to reduce searching time at the expense of a small probability of false positives. With this algorithm, we choose permutation pattern(s) of d attributes to use the first K (K « d) ones to search objects so that they contain relevant attributes with a high probability. We argue that our algorithm can identify the target objects at a false positive rate less than 10^-6 and a few percentages of tree-searching cost compared with a naive d-dimensional searching under a certain condition.