Accelerating <i>m</i> ‐Invariance for Continuous Big Data Publishing

To prevent personal information leakage from the collection and publication of big data, anonymization techniques that protect privacy while preserving data utility are essential. In particular, m ‐invariance provides privacy protection for dynamic datasets that are continuously published as records are updated. However, previous studies have faced challenges where execution time increases with the total dataset size, and no evaluations have been conducted for large‐scale datasets. In our replication of prior research, the anonymization of 1 million dynamic records required an average execution time of approximately 7 h. To address this issue, we propose a novel m ‐invariance algorithm focused on record insertion and deletion, which is independent of the total dataset size. We implemented our algorithm on both CPU and FPGA platforms, achieving execution times of under 5 s for 5000 updates in a 1 million‐record dataset on both platforms. The throughput per updated record was measured at 57 ms/record for the CPU‐based implementation and 2 ms/record for the FPGA‐based implementation. Furthermore, through buffer resource optimization on FPGA, we demonstrated the capability to anonymize large‐scale dynamic datasets exceeding 1 million records. In summary, this study achieves m ‐invariance anonymization for 1 million dynamic records, enabling updates at a rate of 1 s/record. © 2025 The Author(s). IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.