Mask: an Efficient and Tunable Volume-pattern Hiding Algorithm

We present Mask, an efficient and tunable algorithm for hiding volume patterns in multimaps. Volume-pattern leakage, referring to the observable size of data returned by a query, enables adversaries to infer sensitive dataset information, posing severe privacy risks in multimap scenarios. Designed to address this issue, Mask focuses on balancing storage/query overhead and privacy (a key tradeoff in this field), allowing users to define parameters for random data distribution across buckets to realize fine-grained control over storage and query performance, integrating Bloom filters with a bounded cache for efficient indexing (optimizing performance under skewed query workloads); extended to MaskIO for reduced client-side storage, it obfuscates query indexes and uploads them to the server, achieving constant-bounded client storage overhead, and experiments show that Mask outperforms the bucket-based peer Veil with 2 & times; - 4 & times; higher performance and a lower stash ratio (SR).