Memristor-Based Neuromorphic Hardware Improvement for Privacy-Preserving ANN

Because of collecting a large amount of personal data, when the artificial neural network (ANN) is used in human-related topics, it has raised great concerns on privacy preservation. A robust solution is to introduce a noise injection mechanism as differential privacy that promises strong theoretical privacy guarantees. However, privacy-preserving ANN with noisy input data has a substantial risk of reducing the recognition accuracy. Therefore, it is urgently needed to have technologies that can make users' data applied to neural networks while strictly protecting sensitive information. In this paper, a linear optimization (LO) method is proposed to address this accuracy degradation by optimizing the performance of memristor in weight updating processes. Instead of complying with the traditional hardware and algorithm, the LO method calculates update parameters along a piecewise line by using different input pulses. The proposed method can mitigate the nonlinear problem of memristor without prereading the precise current conductance each time, thereby avoiding complex peripheral circuits. The effectiveness of the proposed LO method with two-segment, three-segment, and four-segment models is investigated, respectively. The results show that under different nonlinearity and different perturbation noise required by differential privacy theory, the LO method can increase the recognition accuracy of Modified National Institute of Standards and Technology (MNIST) handwriting digits by 39.67% on average, which provides more space and margin for privacy-preserving technology.