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Secure and Effective Logic Locking for Machine Learning Applications
Logic Locking SAT Attack Machine Learning Applications
2019/1/9
Logic locking has been proposed as a strong protection of intellectual property (IP) against security threats in the IC supply chain especially when the fabrication facility is untrusted. Various tech...
Conditionals in Homomorphic Encryption and Machine Learning Applications
Homomorphic encryption conditionals clashes with fundamental encryption requirements
2018/11/2
Homomorphic encryption has the purpose to allow computations on encrypted data, without the need for decryption other than that of the final result. This could provide an elegant solution to the probl...
Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications
SFE CCS'17
2017/12/1
We present Chameleon, a novel hybrid (mixed-protocol) framework for secure function evaluation (SFE) which enables two parties to jointly compute a function without disclosing their private inputs. Ch...