  Logic locking (LL) has gained attention as a promising intellectual property
protection measure for integrated circuits. However, recent attacks,
facilitated by machine learning (ML), have shown the potential to predict the
correct key in multiple LL schemes by exploiting the correlation of the correct
key value with the circuit structure. This paper presents a generic LL
enhancement method based on a randomized algorithm that can significantly
decrease the correlation between locked circuit netlist and correct key values
in an LL scheme. Numerical results show that the proposed method can
efficiently degrade the accuracy of state-of-the-art ML-based attacks down to
around 50%, resulting in negligible advantage versus random guessing.
