Toward Private Joins on Outsourced Data

In an outsourced database framework, clients place data management responsibilities with specialized service providers. Of essential concern in such frameworks is data privacy. Potential clients are reluctant to outsource sensitive data to a foreign party without strong privacy assurances beyond policy “fine prints.” In this paper, we introduce a mechanism for executing general binary JOIN operations (for predicates that satisfy certain properties) in an outsourced relational database framework with computational privacy and low overhead—the first, to the best of our knowledge. We illustrate via a set of relevant instances of JOIN predicates, including: range and equality (e.g., for geographical data), Hamming distance (e.g., for DNA matching), and semantics (i.e., in health-care scenarios—mapping antibiotics to bacteria). We experimentally evaluate the main overhead components and show they are reasonable. The initial client computation overhead for 100,000 data items is around 5 minutes and our privacy mechanisms can sustain theoretical throughputs of several million predicate evaluations per second, even for an unoptimized OpenSSL-based implementation.