Dynamic local differential privacy location protection method based on critical path

Location information was recognized as a critical personal data asset in the digital age, offering convenient services while simultaneously posing significant risks of privacy breaches. Local differential privacy models, which do not rely on trusted third parties, had garnered widespread attention. However, significant challenges were identified in existing location protection methods, including the difficulty of adapting spatial partitioning to complex location distributions, along with high communication and computational overhead that limited system efficiency. To address these challenges, a dynamic local differential privacy location protection method based on a critical path was proposed. A spatial index adapted to user distribution density was constructed through non-uniform quadtree spatial partitioning and Hilbert curve traversal, which effectively improved data usability. Subsequently, the proposed critical path encoding mechanism was executed on the server side to compress the complex partition structure into concise path information, thereby reducing communication overhead during parameter transmission. On the user side, the Hilbert index encoding of the user’s region was perturbed using a randomized response mechanism under the local differential privacy model to protect the privacy of the original location. On the server side, the collected perturbed location encodings from users were aggregated and analyzed. Based on the spatiotemporal continuity of location distribution, the proposed spatial partition structure dynamic adjustment strategy was then implemented to efficiently adapt to dynamic changes in user distribution at an extremely low computational cost. Experiments conducted on real-world location datasets demonstrate that the proposed method provides improved location data availability and algorithm runtime efficiency while achieving local differential privacy protection for user locations.