Maximum-likelihood decoding of device-specific multi-bit symbols for reliable key generation
We present a PUF key generation scheme that uses the provably optimal method of maximum-likelihood (ML) detection on symbols derived from PUF response bits. Each device forms a noisy, device-specific symbol constellation, based on manufacturing variation. Each detected symbol is a letter in a codewo...
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers (IEEE)
2015
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| Online Access: | http://hdl.handle.net/1721.1/100010 https://orcid.org/0000-0001-8253-7714 |
| _version_ | 1811087897643712512 |
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| author | Yu, Meng-Day Hiller, Matthias Devadas, Srinivas |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Yu, Meng-Day Hiller, Matthias Devadas, Srinivas |
| author_sort | Yu, Meng-Day |
| collection | MIT |
| description | We present a PUF key generation scheme that uses the provably optimal method of maximum-likelihood (ML) detection on symbols derived from PUF response bits. Each device forms a noisy, device-specific symbol constellation, based on manufacturing variation. Each detected symbol is a letter in a codeword of an error correction code, resulting in non-binary codewords. We present a three-pronged validation strategy: i. mathematical (deriving an optimal symbol decoder), ii. simulation (comparing against prior approaches), and iii. empirical (using implementation data). We present simulation results demonstrating that for a given PUF noise level and block size (an estimate of helper data size), our new symbol-based ML approach can have orders of magnitude better bit error rates compared to prior schemes such as block coding, repetition coding, and threshold-based pattern matching, especially under high levels of noise due to extreme environmental variation. We demonstrate environmental reliability of a ML symbol-based soft-decision error correction approach in 28nm FPGA silicon, covering -65°C to 105°C ambient (and including 125°C junction), and with 128bit key regeneration error probability ≤ 1 ppm. |
| first_indexed | 2024-09-23T13:53:38Z |
| format | Article |
| id | mit-1721.1/100010 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T13:53:38Z |
| publishDate | 2015 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1000102022-09-28T16:54:25Z Maximum-likelihood decoding of device-specific multi-bit symbols for reliable key generation Yu, Meng-Day Hiller, Matthias Devadas, Srinivas Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Yu, Meng-Day Devadas, Srinivas We present a PUF key generation scheme that uses the provably optimal method of maximum-likelihood (ML) detection on symbols derived from PUF response bits. Each device forms a noisy, device-specific symbol constellation, based on manufacturing variation. Each detected symbol is a letter in a codeword of an error correction code, resulting in non-binary codewords. We present a three-pronged validation strategy: i. mathematical (deriving an optimal symbol decoder), ii. simulation (comparing against prior approaches), and iii. empirical (using implementation data). We present simulation results demonstrating that for a given PUF noise level and block size (an estimate of helper data size), our new symbol-based ML approach can have orders of magnitude better bit error rates compared to prior schemes such as block coding, repetition coding, and threshold-based pattern matching, especially under high levels of noise due to extreme environmental variation. We demonstrate environmental reliability of a ML symbol-based soft-decision error correction approach in 28nm FPGA silicon, covering -65°C to 105°C ambient (and including 125°C junction), and with 128bit key regeneration error probability ≤ 1 ppm. Bavaria California Technology Center (Grant 2014-1/9) 2015-11-23T17:54:47Z 2015-11-23T17:54:47Z 2015-05 Article http://purl.org/eprint/type/ConferencePaper 978-1-4673-7421-7 http://hdl.handle.net/1721.1/100010 Yu, Meng-Day, Matthias Hiller, and Srinivas Devadas. “Maximum-Likelihood Decoding of Device-Specific Multi-Bit Symbols for Reliable Key Generation.” 2015 IEEE International Symposium on Hardware Oriented Security and Trust (HOST) (May 2015). https://orcid.org/0000-0001-8253-7714 en_US http://dx.doi.org/10.1109/HST.2015.7140233 Proceedings of the 2015 IEEE International Symposium on Hardware Oriented Security and Trust (HOST) Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) MIT web domain |
| spellingShingle | Yu, Meng-Day Hiller, Matthias Devadas, Srinivas Maximum-likelihood decoding of device-specific multi-bit symbols for reliable key generation |
| title | Maximum-likelihood decoding of device-specific multi-bit symbols for reliable key generation |
| title_full | Maximum-likelihood decoding of device-specific multi-bit symbols for reliable key generation |
| title_fullStr | Maximum-likelihood decoding of device-specific multi-bit symbols for reliable key generation |
| title_full_unstemmed | Maximum-likelihood decoding of device-specific multi-bit symbols for reliable key generation |
| title_short | Maximum-likelihood decoding of device-specific multi-bit symbols for reliable key generation |
| title_sort | maximum likelihood decoding of device specific multi bit symbols for reliable key generation |
| url | http://hdl.handle.net/1721.1/100010 https://orcid.org/0000-0001-8253-7714 |
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