Building Disclosure Risk Aware Query Optimizers for Relational Databases

Many DBMS products in the market provide built in encryption support to deal with the security concerns of the organizations. This solution is quite effective in prevent- ing data leakage from compromised/stolen storage devices. However, recent studies show that a significant part of the leaked records have been done so by using specialized malwares that can access the main memory of systems. These malwares can easily capture the sensitive information that are decrypted in the memory including the cryptographic keys used to decrypt them. This can further compromise the security of data residing on disk that are encrypted with the same keys. In this paper we quantify the disclosure risk of encrypted data in a relational DBMS for main memory-based attacks and propose modifications to the standard query processing mechanism to minimize such risks. Specif- ically, we propose query optimization techniques and disclo- sure models to design a data-sensitivity aware query opti- mizer. We implemented a prototype DBMS by modifying both the storage engine and optimizer of MySQL-InnoDB server. The experimental results show that the disclosure risk of such attacks can be reduced dramatically while in- curring a small performance overhead in most cases.