Design of Physically Unclonable Functions Using FPGAs CPRE 583 Michael Patterson, Aaron Mills

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Design of Physically Unclonable Functions Using FPGAs CPRE 583 Michael Patterson, Aaron Mills. What is a PUF?. Hardware hash function Uniquely identify a device “Impossible” to reverse engineer Modeling attacks, power spectrum analysis... Applications: RFID, Chip Authentication, Etc. - PowerPoint PPT Presentation

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Design of Physically Unclonable Functions Using FPGAs

CPRE 583Michael Patterson, Aaron Mills

What is a PUF? Hardware hash function Uniquely identify a device “Impossible” to reverse engineer

Modeling attacks, power spectrum analysis...

Applications: RFID, Chip Authentication, Etc.

Delay-Based PUF Types Butterfly PUF

Requires feedback loops to be identical

Delay-Based PUF Types Arbiter PUF

Again, issue: each “race track” must be identical

Tunable delay, constrain the synthesis placement, etc...

Our Design 16 bit challenge, 8 bit response

Our Design Rationale

– Challenge partitioning

– Number of ROs

– Counter value

– Other comments

RO in vhdlsignal ros : std_logic_vector(4 downto 0);

attribute keep: boolean;

attribute keep of ros: signal is true;

Properties of a good PUF1) Consistency

1) Uniqueness

1) One-to-One

“Consistency” Testing

One PUF, One Challenge:

• PUF is given the same 16-bit challenge 32 times

• repeated for 128 randomly generated challenges.

• the percentage of responses that differ will be calculated

• the ideal value is a 0 percent change.

“Consistency” Results

The 8-bit response was correct 94.68 percent ofthe time.

“Uniqueness” TestingOne PUF, different challenges

• A single PUF is given 1024 different challenges consisting of a Gray Code pattern (a series of numbers that tours unique data values bychanging only one bit at a time).

• The average hamming distance between adjacent responses will be calculated.

• ideal average hamming distance is 50 percent.

“Uniqueness” Results

On average, 2.88 bits change in the response for a onebit change in input.

“One-to-One” TestingDifferent PUFs, one challenge

• Different PUFs given the same series of 1024 challenges.

• The average hamming distance between responses is calculated.

• ideal average hamming distance is 50 percent.

“One-to-One” Results

On average, 46.16 percent of the bits are different between responses of two different PUFs to the same challenge.

Temperature StabilityWhy is considering temperature important?

Linear approximation:

ρ(T) = ρ0[1 + α(T − T0)]

Temperature StabilityTesting

• Same format as Consistency test

• Every 5C from 10C to 65C

Temperature StabilityResults

But...most errors caused by a few inputs

Response Bit 0 1 2 3 4 5 6 7

Ham. Distance (%) 0.3 2 0.1 0.6 0 0 0.2 2

Hard MacrosWhy are hard macros useful?

Issues...

Future Work• Hard Macro Usage

• Post processing to improve results

– Repeat same challenge several times

• Large input size for testing to increase statistical significance of results

• Research properties of different input patterns

• Optimize design to consume less space on the fpga

– Multi-phase calculation

References1. An Analysis of Delay Based PUF Implementations on FPGA, Sergey Morozov, Abhranil Maiti, and Patrick Schaumont

2. Physical Unclonable Functions for Device Authentication and Secret Key Generation, G. E. Suh and S. Devadas.

3. B. Gassend, D. Clarke, M. van Dijk, and S. Devadas. Delay-based circuit authentication and applications.

References4. J. Guajardo, S. Kumar, G.-J. Schrijen, and P.

Tuyls. Physical unclonable functions and public-key crypto for fpga ip protection.

5. S. Kumar, J. Guajardo, R. Maes, G.-J. Schrijen, and P. Tuyls. Extended abstract: The butterfly puf protecting ip on every fpga.

6. M. Majzoobi and F. Koushanfar. Time-bounded authentication of fpgas.

7. G. E. Suh and S. Devadas. Physical unclonable functions for device authentication and secret key generation.

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