Kansas State Researches Information Exchange Security

Researchers at Kansas State University are studying how to keep sensitive information safe when it's "aggressively" exchanged among systems. Computer scientists at the Manhattan-based university are developing high-level policy languages and verification techniques to strengthen the security and integrity of exchange mechanisms.

The ability to guarantee secure information flow is becoming more critical as government and industry push toward increasingly complex information systems, said John Hatcliff, professor of computing and information sciences. "Whether it's healthcare or military information, what people really want is the ability to push information out rapidly to anyone who needs it. You may have a doctor trying to make a diagnosis or a platoon leader trying to coordinate a maneuver in the context of a larger battlefield operation. In either case, more information leads to better decision making and better outcomes. However, you have to make sure as you're aggressively pushing information to decision makers that you don't inadvertently leak sensitive information to someone who shouldn't be seeing it."

Hatcliff is the head of the university's Specification, Analysis and Transformation of Software laboratory. Researchers at the lab do work in security, software engineering, programming language semantics, and automatic analysis of computer software.

Funding for the research came from a five-year, $3 million grant from the Air Force Office of Scientific Research and donations from Rockwell Collins, a company that creates communications and aviation electronics for the defense and aerospace industries. Kansas State is collaborating with researchers at Princeton University on the work.

Currently, the focus is on developing mathematical and logical models to enable designers and analysts to precisely state what information is allowed to flow from one point to another and under what conditions, Hatcliff said. "Then we're building tools to help people use those mathematical techniques to verify that their systems are correct."

The researchers are also creating tools to provide graphical images of information flowing through a system so that designers and auditors can more quickly understand a system's information flow behavior. The research focuses on systems where high levels of assurance are required and where the systems must prove conformance to information flow policies during a certification phase before being deployed.

About the Author

Dian Schaffhauser is a former senior contributing editor for 1105 Media's education publications THE Journal, Campus Technology and Spaces4Learning.

Featured

  • abstract illustration of artificial intelligence

    CSU Shares AI Learnings in Systemwide Survey

    In a systemwide survey of more than 94,000 faculty, staff, and students, California State University recently documented widespread AI use across its 22 campuses.

  • AI logo near computer equipment

    White House Releases National Policy Framework for AI

    The White House has released a four-page AI policy framework aimed at setting a national approach to AI, with priorities including child safety, intellectual property protections, truth and accuracy guardrails, and worker training for an AI-driven economy.

  • Dana Brunson facilitates a roundtable discussion with research and higher education IT leaders

    Internet2: Closing the Access Gap for Research Cyberinfrastructure

    Internet2's Research Engagement Team brings CIOs and other campus technology leadership together with research computing and data facilitators, forming a community that enables research cyberinfrastructure at institutions of all types and sizes.

  • Silhouettes of business professionals stand against a blurred futuristic city skyline at night, with a glowing digital network data connection

    It's Time for Higher Ed to Get Serious About AI Strategy

    Without a coordinated strategy that involves multiple academic and administrative units across the entire campus, colleges risk wasting resources, duplicating efforts, and ultimately failing to deliver on the promise of deploying technology to improve learning and operations.