DFB-PPGSQ: Dynamic Forward/Backward-Private Graph Similarity Matching Query over Encrypted Sensor Graph Databases

Sensor-network, Internet of Things, industrial-monitoring, and cyber-physical security graphs are increasingly outsourced to clouds, where similarity search should be supported without exposing graph content, query graphs, update contents, or database evolution. Existing privacy-preserving graph similarity schemes mainly target static encrypted databases and therefore handle insertions, deletions, label updates, and long-running index maintenance poorly. This paper proposes DFB-PPGSQ, a dynamic forward/backward-private graph similarity matching scheme that moves branch-based lower-bound filtering into a structured-encryption framework. DFB-PPGSQ uses epoch-local feature tokens, per-record occurrence handles, one-time update labels, update buffers, deletion tombstones, and shuffle-based branch-tree re-randomization to preserve pruning efficiency while bounding temporal leakage. We formalize the system model, leakage functions, algorithms, and security interpretation, and implement a reproducible Python prototype with HMAC-SHA256 token generation and multi-profile dynamic sensor-topology workloads. Across five random seeds, DFB-PPGSQ keeps query latency close to the static branch-tree baseline (46.60 ms versus 44.72 ms at 4000 graphs), avoids immediate full-rebuild updates (0.091 ms insertion and 0.092 ms label update), and keeps metadata-assisted cross-epoch token linkage below 5.6% attack success after refresh in additional industrial and campus IoT stress workloads.