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Mask: An Efficient and Tunable Volume-Pattern Hiding Algorithm
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AI
DOI：10.1109/JIOT.2025.3646486.png)
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Abstract
En 中文
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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\times $ </tex-math></inline-formula>–<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4\times $ </tex-math></inline-formula> higher performance and a lower stash ratio (SR).
Keywords:
Constant query overhead
constant-bounded client storage
stash ratio (SR)
volume-pattern hiding
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