IEEE Transactions on Multi-Scale Computing Systems

From the July-September 2016 issue

A Lockdown Technique to Prevent Machine Learning on PUFs for Lightweight Authentication

By Meng-Day (Mandel) Yu, Matthias Hiller, Jeroen Delvaux, Richard Sowell, Srinivas Devadas, and Ingrid Verbauwhede

Featured article thumbnail imageWe present a lightweight PUF-based authentication approach that is practical in settings where a server authenticates a device, and for use cases where the number of authentications is limited over a device's lifetime. Our scheme uses a server-managed challenge/response pair (CRP) lockdown protocol: unlike prior approaches, an adaptive chosen-challenge adversary with machine learning capabilities cannot obtain new CRPs without the server's implicit permission. The adversary is faced with the problem of deriving a PUF model with a limited amount of machine learning training data. Our system-level approach allows a so-called strong PUF to be used for lightweight authentication in a manner that is heuristically secure against today's best machine learning methods through a worst-case CRP exposure algorithmic validation. We also present a degenerate instantiation using a weak PUF that is secure against computationally unrestricted adversaries, which includes any learning adversary, for practical device lifetimes and read-out rates. We validate our approach using silicon PUF data, and demonstrate the feasibility of supporting 10, 1,000, and 1M authentications, including practical configurations that are not learnable with polynomial resources, e.g., the number of CRPs and the attack runtime, using recent results based on the probably-approximately-correct (PAC) complexity-theoretic framework.

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Call for Papers

Special Issue on Emerging Technologies and Architectures for Manycore Computing

Submission Deadline: December 1, 2016. View PDF.

The pursuit of Moore's Law is slowing and the exploration of alternative devices is underway to replace the CMOS transistor and traditional architectures at the heart of data processing. Moreover, the emergence of stringent application constraints, particularly those linked to energy consumption, require new system architectural strategies (e.g. manycore) and real-time operational adaptability approaches. Such complex systems require new and powerful design and programming methods to ensure optimal and reliable operation. This special issue aims at collating new research along all the dimensions of emerging technologies and architectures for computing in manycores.

Special Issue on Cognitive Computing with Emerging Technology

Extended Submission Deadline: December 15, 2016. View PDF.

Over the last several decades, Dennard scaling and Moore’s law have dramatically improved the capabilities of Von Neumann-style computing systems – where “memory” delivers instructions and data to a dedicated “processing unit”. However, as scaling limitations of 2-D ICs are becoming more apparent, there is a growing interest in innovations that will ensure that future computing systems continue to be exponentially-more-capable than the systems of today.

In particular, cognitive computing systems inspired by facets of the human brain such as unsupervised, autonomous and continuous learning, are emerging as a promising alternative. Research in this area often involves cross-disciplinary exploration at multiple scales, combining new materials and devices with novel architectural concepts and integration schemes. Targeting the broad device, circuit, and architecture, as well as nanotechnology research communities, this special issue seeks papers on innovative new concepts for such systems. High-risk high-reward type of ideas, rethinking system design at multiple scales, will be preferred to incremental research. While many of these systems will not rely on non-Von Neumann architectures, the call does not preclude massively parallel systems with conventional hardware components, where novel integration and/or packaging could enable new capabilities such as the high degree of connectivity and collective functions reminiscent of the neocortex and other natural systems.

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