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Logic Locking at the Frontiers of Machine Learning: A Survey on Developments and Opportunities

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arxiv 2107.01915 v4 pith:INB4GTH4 submitted 2021-07-05 cs.CR cs.AI

Logic Locking at the Frontiers of Machine Learning: A Survey on Developments and Opportunities

classification cs.CR cs.AI
keywords lockinglogiclearningmachinedesigndevelopmentsfrontiersopportunities
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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In the past decade, a lot of progress has been made in the design and evaluation of logic locking; a premier technique to safeguard the integrity of integrated circuits throughout the electronics supply chain. However, the widespread proliferation of machine learning has recently introduced a new pathway to evaluating logic locking schemes. This paper summarizes the recent developments in logic locking attacks and countermeasures at the frontiers of contemporary machine learning models. Based on the presented work, the key takeaways, opportunities, and challenges are highlighted to offer recommendations for the design of next-generation logic locking.

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