ANOSY: Approximated Knowledge Synthesis with Refinement Types for Declassification

I’m excited to announce our paper “ANOSY: Approximated Knowledge Synthesis with Refinement Types for Declassification” has been accepted to PLDI 2022.  Programs often need to reveal information about private or sensitive data. For example, consider an advertiser who wants to send an advertisement to users near a restaurant. To accomplish this, the advertiser could write […]

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Room for Disagreement

Modern formats, in brief By and large, when people share a document (whether it’s formatted text, visual media, or some combination), they believe that when they both look at it, they’re seeing the same thing. Modern users have arguably been taught/coerced into tolerating small differences (color pallets that are varying degrees of rich-to-washed-out, poop emojis […]

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Discovering and Mitigating Emergent Computations With Innovative Program Synthesis

One of our previous projects explored how flaws in the design and implementation of systems can introduce “weird machines” that make them vulnerable to exploitation.  Now we have begun a follow-up project, Synthesizing Evidence of Emergent Computation (SEEC). SEEC is part of DARPA’s Artificial Intelligence Mitigations of Emergent Execution (AIMEE) program, whose goal is to […]

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Project Fromager: $12M Project to Apply Zero-Knowledge Proofs to Software Assurance

A zero-knowledge proof (ZKP) is a mathematical tool that provides irrefutable proof of a claim’s validity, without revealing anything else about the claim or the data used to prove it. Today, the application of ZKPs often gravitates towards cryptocurrency transactions, where they can be used to prove that a transaction is valid without revealing details […]

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Providing Safety and Verification for Learning-Enabled Cyber-Physical Systems

  • Matthew Clark

Machine learning has revolutionized cyber-physical systems (CPS) in multiple industries – in the air, on land, and in the deep sea.  And yet, verifying and assuring the safety of advanced machine learning is difficult because of the following reasons:  State-Space Explosion: Autonomous systems are characteristically adaptive, intelligent, and/or may incorporate learning capabilities.  Unpredictable Environments: The […]

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Creating an Assurance Model for Secure Embedded Systems

In 2019, Galois and its spinout Tangram Flex were awarded a $5 million contract for the DARPA I2O Cyber Assured Systems Engineering (CASE) program. We wanted to present an update for the project’s progress.  Introducing Cyber-Assured Plugins to embedded computer systems “The problem is not in our stars … but in ourselves,” is a paraphrase […]

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Building DaeDaLus and ICARUS As We All Try to Stay Out of the Sun

Document and protocol formats are the languages in which computing systems exchange information. The notion of a “document-format” certainly isn’t foreign to most computer users: everyday, vast swaths of users directly create, edit, and share documents and media in formats such as PDF, JPEG, and Word. But in principle, the concept of a “format” is […]

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PIRATE: $7.5M DARPA Contract To Accelerate Secure Application Development

I’m excited to announce we’ve been awarded a  $7.5 million contract by the Defense Advanced Research Projects Agency (DARPA) to work on PIRATE, a set of software development tools for designing and building high-performance, physically-partitioned applications that protect sensitive information.  PIRATE stands for Partitioning Information via Resource-Aware Transformations for Everyone.  The project is part of […]

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Measuring the Privacy of Computations

Secure computation enables users to compute some result without revealing the inputs. Privacy schemes that are shown to only reveal outputs are said to have input privacy. However, learning these outputs still tells you something about the private inputs. The important question is: “how much?” The Defense Advanced Research Projects Agency (DARPA) Brandeis program aims […]

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