计算机科学 ›› 2022, Vol. 49 ›› Issue (6A): 575-580.doi: 10.11896/jsjkx.211100155
王健
WANG Jian
摘要: 反向传播神经网络学习算法已经被广泛地应用在医疗诊断、生物信息学、入侵检测、国土安全等领域。这些应用领域的共同点是,都需要从大量的复杂的数据中抽取模式和预测趋势。在以上这些应用领域中,如何保护敏感数据和个人隐私信息是一个重要的问题。目前已有的反向传播神经网络学习算法,绝大多数都没有考虑在学习过程中如何保护数据的隐私信息。文中为反向传播神经网络提出基于隐私保护的算法,适用于数据被水平分割的情况。在建造神经网络的过程中,需要为训练样本集计算网络权向量。为了保证神经网络学习模型的隐私信息不被泄露,本文提出将权向量分配给所有参与方,使得每个参与方都具有权向量的一部分私有值。在对各层的神经元进行计算时,使用安全多方计算协议,从而保证神经网络权向量的中间值和最终值都是安全的。最后,被建造好的学习模型被所有参与方安全地共享,并且每个参与方可以使用该模型为各自的目标数据预测出相应的输出结果。实验结果表明,所提算法在执行时间和准确度误差上比传统非隐私保护算法更具优越性。
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