Identity management has become increasingly more difficult with biometric big data. Hash-based indexing methods are promising for efficient searches in the high-dimensional space. Geometric hashing is one of the popular methods and has seen many of its variants proposed in the literature for fingerprint identification. Most of them use the same real-valued measures of local geometric invariants for both index creation and feature comparison. In this paper, we propose to build a 3D geometric hash table for storing binary minutiae cylinder codes with access keys that collectively describe the global geometric configuration. The proposed scheme is more robust against sample noise and distortion, and its most computation intensive part can be done efficiently in Hamming space. We perform fingerprint indexing experiments on the public benchmark databases of FVC2002 DB1 and NIST DB14. The results show that the performance of our approach can converge faster to high hit rates with lower penetration rates compared to other hash-based fingerprint indexing methods.