Revisiting Frequency Analysis against Encrypted Deduplication via Statistical Distribution

Abstract

Encrypted deduplication addresses both security and storage efficiency in large-scale storage systems: it ensures that each plaintext is encrypted to a ciphertext by a symmetric key derived from the content of the plaintext, so as to allow deduplication on the ciphertexts derived from duplicate plaintexts. However, the deterministic nature of encrypted deduplication leaks the frequencies of plaintexts, thereby allowing adversaries to launch frequency analysis against encrypted deduplication and infer the ciphertext-plaintext pairs in storage. In this paper, we revisit the security vulnerability of encrypted deduplication due to frequency analysis, and show that encrypted deduplication can be even more vulnerable to the sophisticated frequency analysis attack that exploits the underlying storage workload characteristics. We propose the distribution-based attack, which builds on a statistical approach to model the relative frequency distributions of plaintexts and ciphertexts, and improves the inference precision (i.e., have high confidence on the correctness of inferred ciphertext-plaintext pairs) of the previous attack. We evaluate the new attack against real-world storage workloads and provide insights into its actual damage.