  Emerging memristor computing systems have demonstrated great promise in
improving the energy efficiency of neural network (NN) algorithms. The NN
weights stored in memristor crossbars, however, may face potential theft
attacks due to the nonvolatility of the memristor devices. In this paper, we
propose to protect the NN weights by mapping selected columns of them in the
form of 1's complements and leaving the other columns in their original form,
preventing the adversary from knowing the exact representation of each weight.
The results show that compared with prior work, our method achieves
effectiveness comparable to the best of them and reduces the hardware overhead
by more than 18X.
