Enabling Hidden Frequency Keyword-Based Auditing on Distributed Architectures for a Smart Government

AbstractPublic auditing checks the integrity of outsourced data via random sampling and verifying sample data blocks. In practice, however, users do not pay attention to the entire data set but focus on the integrity of only the part of the data containing keywords of interest. Therefore, the keyword-based auditing paradigm is proposed; it depends entirely on the subjective choice or access habits, which makes it possible for malicious storage servers to analyze the auditing frequency, or reduce redundant backups. For government data, auditing frequency privacy leakage or corruption of any file could be catastrophic. In this paper, we propose a hidden frequency keyword-based auditing scheme for a smart government named HFKA, which is compatible with distributed storage architecture. HFKA leverages a Bloom filter, which adjusts the false positive rate to consider auditing files corresponding to specified keywords and auditing random files obtained via fuzzy matching. To obtain privacy-preserving fuzzy matching, HFKA constructs an index table embedded with update times to retrieve a wide range of files to be audited. This approach is secure against the replay attack and supports the index table update through structure iteration instead of recalculation. HFKA provides storage robustness, privacy protection of hidden frequencies, and data security. Additionally, HFKA can reduce audit computation overhead by 32.6% compared to the probabilistic public auditing. KeywordsKeyword-based auditingFrequency hidingDistributed architectureSmart governmentFuzzy matching