Cryptographic techniques have the potential to enable distrusting parties to collaborate in fundamentally new ways, but their practical implementation poses numerous challenges. An important class of such cryptographic techniques is known as secure multi-party computation (MPC). Deploying secure MPC protocols in realistic scenarios requires extensive knowledge spanning multiple areas of cryptography and systems even for seemingly simple applications. And while the steps to arrive at a solution for a particular application are pedestrian, it remains difficult to make the implementation efficient, and cumbersome to apply those same steps to a slightly different application from scratch. Hence, it is an important problem to design an ecosystem for building secure MPC applications with minimum effort and using techniques accessible to non-experts in cryptography.In an effort to provide such an ecosystem for building secure MPC applications using higher degrees of automation, we present the HACCLE (High Assurance Compositional Cryptography: Languages and Environments) toolchain. The HACCLE toolchain contains an embedded domain-specific language (Harpoon) for software developers without cryptographic expertise to write MPC-based programs. Harpoon programs are compiled into acyclic circuits represented in HACCLE's Intermediate Representation (HIR) that serves as an abstraction for implementing a computation using different cryptographic protocols such as secret sharing, homomorphic encryption, or garbled circuits. Implementations of different cryptographic protocols serve as different backends of our toolchain. The extensible design of HIR allows cryptographic experts to plug in new primitives and protocols to realize computations.We have implemented HACCLE, and used it to program interesting algorithms and applications (e.g., secure auction, matrix-vector multiplication, and merge sort). We show that the performance is improved by using our optimization strategies and heuristics.
Abstract. Concerns over efficiency and expressiveness of functional languages have motivated the study of languages that allow state and pure functionality to coexist peacefully. However, state-oriented features complicate the static analyses which are essential for efficient compilation of these languages. The problem is an interesting one because it combines traditional strictness analysis with the abstract storage structure analysis familiar from imperative languages. We apply the technique of abstract interpretation to perform strictness analysis in the Imperative Lambda Calculus of Swamp, Reddy, and Ireland. A basic analysis is presented, along with some extensions to handle certain evident weaknesses; proofs for these analyses are discussed in some detail.
Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden to Washington Headquarters Service, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302, and PCAA is a dynamic, adaptive cognitive architecture that makes previously intractable approximation tasks tractable for NP-hard cognitive problems. PCAA consists of: linear composable cognitive agents, a cognitive mark-up language for cognitive behavior definition, a cognitive layer for derivation of cognitive services and specialized cognitive agents, and a next generation polymorphic hardware and software layer for runtime composition and instantiation of cognitive agents. PCAA is a dynamic, adaptive cognitive architecture that makes previously intractable approximation tasks tractable for NP-hard cognitive problems. PCAA consists of: linear composable cognitive agents, a cognitive mark-up language for cognitive behavior definition, a cognitive layer for derivation of cognitive services and specialized cognitive agents, and a next generation polymorphic hardware and software layer for runtime composition and instantiation of cognitive agents. SPONSOR/MONITOR'S ACRONYM(S) 9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES)AFRL SUBJECT TERMSOur approach included a comprehensive concept study in the context of representative DoD challenge problems that have a clear and well-defined need for ACIP technology. PCAA application experiments demonstrated clear performance improvements over traditional computing architectures for cognitive processing for these applications. Our innovations include:• Dynamically composable hardware and software with linear scalability for cognitive processing across a massively parallel hardware fabric for real time autonomous systems. • A dynamically composed agent architecture that partitions reactive and predefined behaviors into linear lower level cognitive agents that tailor and adapt the overall behavior of the computing architecture to immediate mission needs.• Run-time derived cognitive virtual machines to partition cognitive processing to a new generation of computing run-time configured hardware and software to allow for dynamic cognitive computing reconfiguration required to achieve reactive processing.Our research was driven by DoD applications that have demonstrated needs for diverse cognitive processing that cannot be addressed by current computing hardware and software architectures. We demonstrated our end-to-end approach for two applications with direct DoD relevance: control of autonomous Unmanned Aerial Vehicles and Intelligence Analysis.ii
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