We present OasisDB, an oblivious and scalable RDBMS framework designed to securely manage relational data while protecting against access and volume pattern attacks. Inspired by plaintext RDBMSs, OasisDB leverages existing oblivious key value stores (KV-stores) as storage engines and securely scales them to enhance performance. Its novel multi-tier architecture allows for independent scaling of each tier while supporting multi-user environments without compromising privacy. We demonstrate OasisDB's flexibility by deploying it with two distinct oblivious KV-stores, PathORAM and Waffle, and show its capability to execute a variety of SQL queries, including point and range queries, joins, aggregations, and (limited) updates. Experimental evaluations on the Epinions dataset show that OasisDB scales linearly with the number of machines. When deployed with a plaintext KV-store, OasisDB introduces negligible overhead in its multi-tier architecture compared to a plaintext database, CockroachDB. We also compare OasisDB with ObliDB and Obliviator, two oblivious RDBMSs, highlighting its advantages with scalability and multi-user support.

Encrypted search algorithms (ESAs) enable private search on encrypted data and can be constructed from a variety of cryptographic primitives. All knownsub-linear ESA algorithms leak information and, therefore, the design of leakage attacks is an important way to ascertain whether a given leakage profile is exploitable in practice. Recently,Oya and Kerschbaum(Usenix '22) presented an attack called IHOP that targets the query equality pattern which reveals if and when two queries are for the same keyword of a sequence of dependent queries. In this work, we continue the study of query equality leakage on dependent queries and present two new attacks in this setting which can work either as known-distribution or known-sample attacks. They model query distributions as Markov processes and leverage insights and techniques from stochastic processes and machine learning. We implement our attacks and evaluate them on real-world query logs. Our experiments show that they outperform the state-of-the-art in most settings but also have limitations inpractical settings.

An encrypted search algorithm (ESA) allows a user to encrypt its data while preserving the ability to search over it. As all practical solutions leak some information, cryptanalysis plays an important role in the area of encrypted search. Starting with the work of Islam et al. (NDSS'12), many attacks have been proposed that exploit different leakage profiles under various assumptions. While these attacks improve our understanding of leakage, it can sometimes be difficult to draw definite conclusions about their practical performance. This is due to several reasons, including a lack of open-source implementations (which are needed to reproduce results), empirical evaluations that are conducted on restricted datasets, and in some cases reliance on relatively strong assumptions that can significantly affect accuracy. In this work, we address these limitations. First, we design and implement LEAKER, an open-source framework that evaluates the major leakage attacks against any dataset and that we hope will serve the community as a common way to evaluate leakage attacks. We identify new real-world datasets that capture different use cases for ESAs and, for the first time, include real-world user queries. Finally, we use LEAKER to systematically evaluate known attacks on our datasets, uncovering sometimes unexpected properties that increase or diminish accuracy. Our evaluation shows that some attacks work better on real-world data than previously thought and that others perform worse.