No abstract

Recent years have seen an increased interest towards strong security primitives for encrypted databases (such as oblivious protocols), that hide the access patterns of query execution, and reveal only the volume of results. However, recent work has shown that even volume leakage can enable the reconstruction of entire columns in the database. Yet, existing attacks rely on a set of assumptions that are unrealistic in practice: for example, they (i) require a large number of queries to be issued by the user, or (ii) assume certain distributions on the queries or underlying data (e.g., that the queries are distributed uniformly at random, or that the database does not contain missing values).In this work, we present new attacks for recovering the content of individual user queries, assuming no leakage from the system except the number of results and avoiding the limiting assumptions above. Unlike prior attacks, our attacks require only a single query to be issued by the user for recovering the keyword. Furthermore, our attacks make no assumptions about the distribution of issued queries or the underlying data. Instead, our key insight is to exploit the behavior of real-world applications.We start by surveying 11 applications to identify two key characteristics that can be exploited by attackers-(i) file injection, and (ii) automatic query replay. We present attacks that leverage these two properties in concert with volume leakage, independent of the details of any encrypted database system. Subsequently, we perform an attack on the real Gmail web client by simulating a server-side adversary. Our attack on Gmail completes within a matter of minutes, demonstrating the feasibility of our techniques. We also present three ancillary attacks for situations when certain mitigation strategies are employed.

No abstract

Modern analytical database systems predominantly rely on column-oriented storage, which offers superior compression efficiency due to the nature of the columnar layout. This compression, however, creates challenges in decoding speed during query processing. Previous research has explored predicate pushdown on encoded values to avoid decoding, but these techniques are restricted to specific encoding schemes and predicates, limiting their practical use. In this paper, we propose a generic predicate pushdown approach that supports arbitrary predicates by leveraging selection pushdown to reduce decoding costs. At the core of our approach is a fast select operator capable of directly extracting selected encoded values without decoding, by using Bit Manipulation Instructions, an instruction set extension to the X86 architecture. We empirically evaluate the proposed techniques in the context of Apache Parquet using both micro-benchmarks and the TPC-H benchmark, and show that our techniques improve the query performance of Parquet by up to one order of magnitude with representative scan queries. Further experimentation using Apache Spark demonstrates speed improvements of up to 5.5X even for end-to-end queries involving complex joins.