The popularity of cloud computing has made outsourced databases prevalent in real-world applications. To protect data security, numerous encrypted outsourced databases have been proposed for this paradigm. However, the maintenance of encrypted databases (EDBs) has scarcely been addressed. In this paper, we focus on a typical maintenance task - functional dependency (FD) discovery. We develop novel FD protocols in EDBs while guaranteeing minimal leakages: nothing is revealed besides the database size and the actual discovered FDs. As far as we know, we are the first to formally define secure FD discovery with minimal leakage. We present two oblivious FD discovery protocols and prove them secure in the presence of the persistent adversary (monitoring processes on the server). The first protocol leverages oblivious RAM (ORAM) and is suitable for dynamic databases. The second protocol relies on oblivious sorting and is more practical in static databases due to high parallelism. We present a thorough experimental evaluation of the proposed methods.

As organizations struggle with processing vast amounts of information, outsourcing sensitive data to third parties becomes a necessity. To protect the data, various cryptographic techniques are used in outsourced database systems to ensure data privacy, while allowing efficient querying. A rich collection of attacks on such systems has emerged. Even with strong cryptography, just communication volume or access pattern is enough for an adversary to succeed.

MOTIVATION: The complexity of protein-protein interactions (PPIs) is further compounded by the fact that an average protein consists of two or more domains, structurally and evolutionary independent subunits. Experimental studies have demonstrated that an interaction between a pair of proteins is not carried out by all domains constituting each protein, but rather by a select subset. However, determining which domains from each protein mediate the corresponding PPI is a challenging task. RESULTS: Here, we present domain interaction statistical potential (DISPOT), a simple knowledge-based statistical potential that estimates the propensity of an interaction between a pair of protein domains, given their structural classification of protein (SCOP) family annotations. The statistical potential is derived based on the analysis of >352 000 structurally resolved PPIs obtained from DOMMINO, a comprehensive database of structurally resolved macromolecular interactions. AVAILABILITY AND IMPLEMENTATION: DISPOT is implemented in Python 2.7 and packaged as an open-source tool. DISPOT is implemented in two modes, basic and auto-extraction. The source code for both modes is available on GitHub: https://github.com/korkinlab/dispot and standalone docker images on DockerHub: https://hub.docker.com/r/korkinlab/dispot. The web server is freely available at http://dispot.korkinlab.org/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Database query evaluation over encrypted data can allow database users to maintain the privacy of their data while outsourcing data processing. Order-Preserving Encryption (OPE) and Order-Revealing Encryption (ORE) were designed to enable efficient query execution, but provide only partial privacy. More private protocols, based on Searchable Symmetric Encryption (SSE), Oblivious RAM (ORAM) or custom encrypted data structures, have also been designed. In this paper, we develop a framework to provide the first comprehensive comparison among a number of range query protocols that ensure varying levels of privacy of user data. We evaluate five ORE-based and five generic range query protocols. We analyze and compare them both theoretically and experimentally and measure their performance over database indexing and query evaluation. We report not only execution time but also I/O performance, communication amount, and usage of cryptographic primitive operations. Our comparison reveals some interesting insights concerning the relative security and performance of these approaches in database settings.