| Title | EncDBDB: Searchable Encrypted, Fast, Compressed, In-Memory Database Using Enclaves |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Fuhry, Benny, Jayanth Jain, H A, Kerschbaum, Florian |
| Conference Name | 2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) |
| Keywords | data privacy, data warehouses, Databases, Dictionaries, Encryption, Human Behavior, Memory management, Metrics, outsourced database security, outsourcing, pubcrawl, resilience, Resiliency, Scalability |
| Abstract | Data confidentiality is an important requirement for clients when outsourcing databases to the cloud. Trusted execution environments, such as Intel SGX, offer an efficient solution to this confidentiality problem. However, existing TEE-based solutions are not optimized for column-oriented, in-memory databases and pose impractical memory requirements on the enclave. We present EncDBDB, a novel approach for client-controlled encryption of a column-oriented, in-memory databases allowing range searches using an enclave. EncDBDB offers nine encrypted dictionaries, which provide different security, performance, and storage efficiency tradeoffs for the data. It is especially suited for complex, read-oriented, analytic queries as present, e.g., in data warehouses. The computational overhead compared to plaintext processing is within a millisecond even for databases with millions of entries and the leakage is limited. Compressed encrypted data requires less space than a corresponding plaintext column. Furthermore, EncDBDB's enclave is very small reducing the potential for security-relevant implementation errors and side-channel leakages. |
| DOI | 10.1109/DSN48987.2021.00054 |
| Citation Key | fuhry_encdbdb_2021 |
EncDBDB: Searchable Encrypted, Fast, Compressed, In-Memory Database Using Enclaves