Privacy-preserving Searchable Encryption Based on Anonymization and Differential privacy
MA Cai-xia, Chunfu Jia, Ruizhong Du, Guanxiong Ha, Mingyue Li · 2024
With the rapid development of cloud computing, more and more users are storing sensitive data on cloud servers, making the privacy-preserving of data particularly important. Dynamic searchable symmetric encryption enables efficient retrieval of encrypted data in cloud computing environments while preserving data privacy. However, existing solutions are not effective in defending against various query-recovery attacks. Therefore, this paper focuses on the privacy-preserving of dynamic searchable symmetric encryption, and proposes a privacy-preserving dynamic searchable symmetric encryption based on anonymization and differential privacy – DADP. Firstly, the original indexes are synthesized into fake indexes using the anonymization hash technology. The synthetic indexes possess randomness and irreversibility, making it impossible for adversaries to infer the generation process of the synthetic indexes or recover the original indexes. Additionally, by using differential privacy to process composite indexes, the privacy of keywords and index information is protected, preventing adversaries from inferring sensitive information based on query results. This approach provides dual privacy-preserving. Compared to other schemes, our scheme achieves type-I backward privacy and can withstand seven types of query recovery attacks. And it improves update and query efficiency by 10-100 times.