The security of order-revealing encryption (ORE) has been unclear since its invention. Dataset characteristics for which ORE is especially insecure have been identified, such as small message spaces and low-entropy distributions. On the other hand, properties like one-wayness on uniformly-distributed datasets have been proved for ORE constructions.

Generic programming allows the concise expression of algorithms that would otherwise require large amounts of handwritten code. A number of such systems have been developed over the years, but a common drawback of these systems is poor runtime performance relative to handwritten, non-generic code. Generic-programming systems vary significantly in this regard, but few consistently match the performance of handwritten code. This poses a dilemma for developers. Generic-programming systems offer concision but cost performance. Handwritten code offers performance but costs concision.

Secure multiparty computation (SMC) permits a collection of parties to compute a collaborative result, without any of the parties gaining any knowledge about the inputs provided by other parties. Specifications for SMC are commonly presented as boolean circuits, where optimizations come mostly from reducing the number of multiply-operations (including and-gates) - these are the operations which incur significant cost, either in computation overhead or in communication between the parties. Instead, we take a language-oriented approach, and consequently are able to explore many other kinds of optimizations. We present an efficient and general purpose SMC table-lookup algorithm that can serve as a direct alternative to circuits. Looking up a private (i.e. shared, or encrypted) n-bit argument in a public table requires log(n) parallel-and operations. We use the advanced encryption standard algorithm (AES) as a driving motivation, and by introducing different kinds of parallelization techniques, produce the fastest current SMC implementation of AES, improving the best previously reported results by well over an order of magnitude.